Initial commit

code/keras_contrib/utils/conv_utils.py

+import keras.backend as K
+
+
+def conv_output_length(input_length, filter_size,
+                       padding, stride, dilation=1):
+    """Determines output length of a convolution given input length.
+
+    Copy of the function of keras-team/keras because it's not in the public API
+    So we can't use the function in keras-team/keras to test tf.keras
+
+    # Arguments
+        input_length: integer.
+        filter_size: integer.
+        padding: one of `"same"`, `"valid"`, `"full"`.
+        stride: integer.
+        dilation: dilation rate, integer.
+
+    # Returns
+        The output length (integer).
+    """
+    if input_length is None:
+        return None
+    assert padding in {'same', 'valid', 'full', 'causal'}
+    dilated_filter_size = filter_size + (filter_size - 1) * (dilation - 1)
+    if padding == 'same':
+        output_length = input_length
+    elif padding == 'valid':
+        output_length = input_length - dilated_filter_size + 1
+    elif padding == 'causal':
+        output_length = input_length
+    elif padding == 'full':
+        output_length = input_length + dilated_filter_size - 1
+    return (output_length + stride - 1) // stride
+
+
+def normalize_data_format(value):
+    """Checks that the value correspond to a valid data format.
+
+    Copy of the function in keras-team/keras because it's not public API.
+
+    # Arguments
+        value: String or None. `'channels_first'` or `'channels_last'`.
+
+    # Returns
+        A string, either `'channels_first'` or `'channels_last'`
+
+    # Example
+    ```python
+        >>> from keras import backend as K
+        >>> K.normalize_data_format(None)
+        'channels_first'
+        >>> K.normalize_data_format('channels_last')
+        'channels_last'
+    ```
+
+    # Raises
+        ValueError: if `value` or the global `data_format` invalid.
+    """
+    if value is None:
+        value = K.image_data_format()
+    data_format = value.lower()
+    if data_format not in {'channels_first', 'channels_last'}:
+        raise ValueError('The `data_format` argument must be one of '
+                         '"channels_first", "channels_last". Received: ' +
+                         str(value))
+    return data_format

code/keras_contrib/utils/save_load_utils.py

+import warnings
+
+import h5py
+import keras.backend as K
+from keras import optimizers
+from keras.engine import saving
+
+
+def save_all_weights(model, filepath, include_optimizer=True):
+    """
+    Save model weights and optimizer weights but not configuration to a HDF5 file.
+    Functionally between `save` and `save_weights`.
+
+    The HDF5 file contains:
+        - the model's weights
+        - the model's optimizer's state (if any)
+    If you have a complicated model or set of models that do not serialize
+    to JSON correctly, use this method.
+    # Arguments
+        model: Keras model instance to be saved.
+        filepath: String, path where to save the model.
+        include_optimizer: If True, save optimizer's state together.
+    # Raises
+        ImportError: if h5py is not available.
+    """
+    if h5py is None:
+        raise ImportError('`save_all_weights` requires h5py.')
+
+    with h5py.File(filepath, 'w') as f:
+        model_weights_group = f.create_group('model_weights')
+        model_layers = model.layers
+        saving.save_weights_to_hdf5_group(model_weights_group, model_layers)
+
+        if include_optimizer and hasattr(model, 'optimizer') and model.optimizer:
+            if isinstance(model.optimizer, optimizers.TFOptimizer):
+                warnings.warn(
+                    'TensorFlow optimizers do not '
+                    'make it possible to access '
+                    'optimizer attributes or optimizer state '
+                    'after instantiation. '
+                    'As a result, we cannot save the optimizer '
+                    'as part of the model save file.'
+                    'You will have to compile your model again after loading it. '
+                    'Prefer using a Keras optimizer instead '
+                    '(see keras.io/optimizers).')
+            else:
+                # Save optimizer weights.
+                symbolic_weights = getattr(model.optimizer, 'weights')
+                if symbolic_weights:
+                    optimizer_weights_group = f.create_group('optimizer_weights')
+                    weight_values = K.batch_get_value(symbolic_weights)
+                    weight_names = []
+                    for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)):
+                        # Default values of symbolic_weights is /variable for theano
+                        if K.backend() == 'theano':
+                            if hasattr(w, 'name') and w.name != "/variable":
+                                name = str(w.name)
+                            else:
+                                name = 'param_' + str(i)
+                        else:
+                            if hasattr(w, 'name') and w.name:
+                                name = str(w.name)
+                            else:
+                                name = 'param_' + str(i)
+                        weight_names.append(name.encode('utf8'))
+                    optimizer_weights_group.attrs['weight_names'] = weight_names
+                    for name, val in zip(weight_names, weight_values):
+                        param_dset = optimizer_weights_group.create_dataset(
+                            name,
+                            val.shape,
+                            dtype=val.dtype)
+                        if not val.shape:
+                            # scalar
+                            param_dset[()] = val
+                        else:
+                            param_dset[:] = val
+
+
+def load_all_weights(model, filepath, include_optimizer=True):
+    """Loads the weights of a model saved via `save_all_weights`.
+    If model has been compiled, optionally load its optimizer's weights.
+    # Arguments
+        model: instantiated model with architecture matching the saved model.
+            Compile the model beforehand if you want to load optimizer weights.
+        filepath: String, path to the saved model.
+    # Returns
+        None. The model will have its weights updated.
+    # Raises
+        ImportError: if h5py is not available.
+        ValueError: In case of an invalid savefile.
+    """
+    if h5py is None:
+        raise ImportError('`load_all_weights` requires h5py.')
+
+    with h5py.File(filepath, mode='r') as f:
+        # set weights
+        saving.load_weights_from_hdf5_group(f['model_weights'], model.layers)
+        # Set optimizer weights.
+        if (include_optimizer
+                and 'optimizer_weights' in f and hasattr(model, 'optimizer')
+                and model.optimizer):
+            optimizer_weights_group = f['optimizer_weights']
+            optimizer_weight_names = [n.decode('utf8') for n in
+                                      optimizer_weights_group.attrs['weight_names']]
+            optimizer_weight_values = [optimizer_weights_group[n] for n in
+                                       optimizer_weight_names]
+            model.optimizer.set_weights(optimizer_weight_values)

code/keras_contrib/utils/test_utils.py

+"""Utilities related to Keras unit tests."""
+import sys
+import numpy as np
+from numpy.testing import assert_allclose
+import inspect
+
+import keras
+from keras.layers import Input
+from keras.models import Model
+from keras import backend as K
+
+
+def get_test_data(num_train=1000, num_test=500, input_shape=(10,),
+                  output_shape=(2,),
+                  classification=True, num_classes=2):
+    """Generates test data to train a model on.
+
+    classification=True overrides output_shape
+    (i.e. output_shape is set to (1,)) and the output
+    consists in integers in [0, num_class-1].
+
+    Otherwise: float output with shape output_shape.
+    """
+    samples = num_train + num_test
+    if classification:
+        y = np.random.randint(0, num_classes, size=(samples,))
+        X = np.zeros((samples,) + input_shape)
+        for i in range(samples):
+            X[i] = np.random.normal(loc=y[i], scale=0.7, size=input_shape)
+    else:
+        y_loc = np.random.random((samples,))
+        X = np.zeros((samples,) + input_shape)
+        y = np.zeros((samples,) + output_shape)
+        for i in range(samples):
+            X[i] = np.random.normal(loc=y_loc[i], scale=0.7, size=input_shape)
+            y[i] = np.random.normal(loc=y_loc[i], scale=0.7, size=output_shape)
+
+    return (X[:num_train], y[:num_train]), (X[num_train:], y[num_train:])
+
+
+def layer_test(layer_cls, kwargs={}, input_shape=None, input_dtype=None,
+               input_data=None, expected_output=None,
+               expected_output_dtype=None, fixed_batch_size=False):
+    """Test routine for a layer with a single input tensor
+    and single output tensor.
+
+    Copy of the function in keras-team/keras because it's not in the public API.
+    If we use the one from keras-team/keras it won't work with tf.keras.
+    """
+    # generate input data
+    if input_data is None:
+        assert input_shape
+        if not input_dtype:
+            input_dtype = K.floatx()
+        input_data_shape = list(input_shape)
+        for i, e in enumerate(input_data_shape):
+            if e is None:
+                input_data_shape[i] = np.random.randint(1, 4)
+        input_data = (10 * np.random.random(input_data_shape))
+        input_data = input_data.astype(input_dtype)
+    else:
+        if input_shape is None:
+            input_shape = input_data.shape
+        if input_dtype is None:
+            input_dtype = input_data.dtype
+    if expected_output_dtype is None:
+        expected_output_dtype = input_dtype
+
+    # instantiation
+    layer = layer_cls(**kwargs)
+
+    # test get_weights , set_weights at layer level
+    weights = layer.get_weights()
+    layer.set_weights(weights)
+
+    expected_output_shape = layer.compute_output_shape(input_shape)
+
+    # test in functional API
+    if fixed_batch_size:
+        x = Input(batch_shape=input_shape, dtype=input_dtype)
+    else:
+        x = Input(shape=input_shape[1:], dtype=input_dtype)
+    y = layer(x)
+    assert K.dtype(y) == expected_output_dtype
+
+    # check with the functional API
+    model = Model(x, y)
+
+    actual_output = model.predict(input_data)
+    actual_output_shape = actual_output.shape
+    for expected_dim, actual_dim in zip(expected_output_shape,
+                                        actual_output_shape):
+        if expected_dim is not None:
+            assert expected_dim == actual_dim
+
+    if expected_output is not None:
+        assert_allclose(actual_output, expected_output, rtol=1e-3)
+
+    # test serialization, weight setting at model level
+    model_config = model.get_config()
+    custom_objects = {layer.__class__.__name__: layer.__class__}
+    recovered_model = model.__class__.from_config(model_config, custom_objects)
+    if model.weights:
+        weights = model.get_weights()
+        recovered_model.set_weights(weights)
+        _output = recovered_model.predict(input_data)
+        assert_allclose(_output, actual_output, rtol=1e-3)
+
+    # test training mode (e.g. useful when the layer has a
+    # different behavior at training and testing time).
+    if has_arg(layer.call, 'training'):
+        model.compile('rmsprop', 'mse')
+        model.train_on_batch(input_data, actual_output)
+
+    # test instantiation from layer config
+    layer_config = layer.get_config()
+    layer_config['batch_input_shape'] = input_shape
+    layer = layer.__class__.from_config(layer_config)
+
+    # for further checks in the caller function
+    return actual_output
+
+
+def has_arg(fn, name, accept_all=False):
+    """Checks if a callable accepts a given keyword argument.
+
+    For Python 2, checks if there is an argument with the given name.
+
+    For Python 3, checks if there is an argument with the given name, and
+    also whether this argument can be called with a keyword (i.e. if it is
+    not a positional-only argument).
+
+    This function is a copy of the one in keras-team/keras because it's not
+    in the public API.
+
+    # Arguments
+        fn: Callable to inspect.
+        name: Check if `fn` can be called with `name` as a keyword argument.
+        accept_all: What to return if there is no parameter called `name`
+                    but the function accepts a `**kwargs` argument.
+
+    # Returns
+        bool, whether `fn` accepts a `name` keyword argument.
+    """
+    if sys.version_info < (3,):
+        arg_spec = inspect.getargspec(fn)
+        if accept_all and arg_spec.keywords is not None:
+            return True
+        return name in arg_spec.args
+    elif sys.version_info < (3, 3):
+        arg_spec = inspect.getfullargspec(fn)
+        if accept_all and arg_spec.varkw is not None:
+            return True
+        return (name in arg_spec.args or
+                name in arg_spec.kwonlyargs)
+    else:
+        signature = inspect.signature(fn)
+        parameter = signature.parameters.get(name)
+        if parameter is None:
+            if accept_all:
+                for param in signature.parameters.values():
+                    if param.kind == inspect.Parameter.VAR_KEYWORD:
+                        return True
+            return False
+        return (parameter.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD,
+                                   inspect.Parameter.KEYWORD_ONLY))
+
+
+def to_list(x, allow_tuple=False):
+    if isinstance(x, list):
+        return x
+    if allow_tuple and isinstance(x, tuple):
+        return list(x)
+    return [x]
+
+
+def unpack_singleton(x):
+    if len(x) == 1:
+        return x[0]
+    return x
+
+
+if keras.__name__ == 'keras':
+    is_tf_keras = False
+elif keras.__name__ == 'tensorflow.keras':
+    is_tf_keras = True
+else:
+    raise KeyError('Cannot detect if using keras or tf.keras.')
+
+
+def to_tuple(shape):
+    """This functions is here to fix an inconsistency between keras and tf.keras.
+
+    In tf.keras, the input_shape argument is an tuple with `Dimensions` objects.
+    In keras, the input_shape is a simple tuple of ints or `None`.
+
+    We'll work with tuples of ints or `None` to be consistent
+    with keras-team/keras. So we must apply this function to
+    all input_shapes of the build methods in custom layers.
+    """
+    if is_tf_keras:
+        import tensorflow as tf
+        return tuple(tf.TensorShape(shape).as_list())
+    else:
+        return shape

code/model_utils.py

+import keras
+import tensorflow as tf
+from keras.callbacks import Callback
+import matplotlib.pyplot as plt
+import numpy as np
+import os, time
+from keras.regularizers import l1, l2
+import keras.backend as K
+from keras.layers import Input, Dense, Dropout, BatchNormalization, Conv2D, MaxPooling2D, AveragePooling2D, concatenate, \
+    Activation, ZeroPadding2D
+from keras.layers import add, Flatten
+from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization
+from keras.layers import Layer, InputSpec
+from keras import initializers, regularizers, constraints
+from keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D, Reshape, Dense, multiply, Permute, Concatenate, \
+    Conv2D, Add, Activation, Lambda
+import keras
+from keras.activations import sigmoid
+
+
+def plain_cnn_256ms(input_layer, nb_classes):
+    x = input_layer
+
+    x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='same')
+    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
+
+    x = identity_Block(x, nb_filter=64, kernel_size=(3, 3), strides=(1, 1))
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2))
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 1))
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 1))
+
+    x = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
+    x = Flatten()(x)
+
+    x = keras.layers.Dropout(0.5)(x)
+    regularSet = keras.regularizers.l1_l2(l1=0.01, l2=0.01)
+    x = keras.layers.Dense(8, activation="sigmoid", kernel_regularizer=regularSet)(x)
+
+    x = Dense(nb_classes)(x)
+    x = Activation('softmax')(x)
+
+    model = keras.models.Model(inputs=input_layer, outputs=x)
+    return model
+
+
+def resnet_256ms(input_layer, nb_classes):
+    x = input_layer
+
+    x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='same')
+    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
+
+    x = identity_Block(x, nb_filter=64, kernel_size=(3, 3), strides=(1, 1), use_shortcut=True, with_conv_shortcut=True)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2), use_shortcut=True, with_conv_shortcut=True)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 1), use_shortcut=True, with_conv_shortcut=True)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 1), use_shortcut=True, with_conv_shortcut=True)
+
+    x = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
+    x = Flatten()(x)
+
+    x = keras.layers.Dropout(0.5)(x)
+    regularSet = keras.regularizers.l1_l2(l1=0.01, l2=0.01)
+    x = keras.layers.Dense(8, activation="sigmoid", kernel_regularizer=regularSet)(x)
+
+    x = Dense(nb_classes)(x)
+    x = Activation('softmax')(x)
+
+    model = keras.models.Model(inputs=input_layer, outputs=x)
+    return model
+
+
+def resnet_CBAM_256ms(input_layer, nb_classes):
+    x = input_layer
+
+    x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='same')
+    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
+
+    x = identity_Block(x, nb_filter=64, kernel_size=(3, 3), strides=(1, 1), use_shortcut=True, with_conv_shortcut=True)
+    x = CbamBlock2D().layer(x)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2), use_shortcut=True, with_conv_shortcut=True)
+    x = CbamBlock2D().layer(x)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 1), use_shortcut=True, with_conv_shortcut=True)
+    x = CbamBlock2D().layer(x)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 1), use_shortcut=True, with_conv_shortcut=True)
+    x = CbamBlock2D().layer(x)
+
+    x = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
+    x = Flatten()(x)
+
+    x = keras.layers.Dropout(0.5)(x)
+    regularSet = keras.regularizers.l1_l2(l1=0.01, l2=0.01)
+    x = keras.layers.Dense(8, activation="relu", kernel_regularizer=regularSet)(x)
+
+    x = Dense(nb_classes)(x)
+    x = Activation('softmax')(x)
+
+    model = keras.models.Model(inputs=input_layer, outputs=x)
+    return model
+
+
+def plain_cnn_128ms(input_layer, nb_classes):
+    x = input_layer
+
+    x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='same')
+    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
+
+    x = identity_Block(x, nb_filter=64, kernel_size=(3, 3), strides=(1, 1))
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2))
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 2))
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 1))
+
+    x = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
+    x = Flatten()(x)
+
+    x = keras.layers.Dropout(0.5)(x)
+    regularSet = keras.regularizers.l1_l2(l1=0.01, l2=0.01)
+    x = keras.layers.Dense(8, activation="sigmoid", kernel_regularizer=regularSet)(x)
+
+    x = Dense(nb_classes)(x)
+    x = Activation('softmax')(x)
+
+    model = keras.models.Model(inputs=input_layer, outputs=x)
+    return model
+
+
+def resnet_128ms(input_layer, nb_classes):
+    x = input_layer
+
+    x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='same')
+    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
+
+    x = identity_Block(x, nb_filter=64, kernel_size=(3, 3), strides=(1, 1), use_shortcut=True, with_conv_shortcut=True)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2), use_shortcut=True, with_conv_shortcut=True)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 2), use_shortcut=True, with_conv_shortcut=True)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 1), use_shortcut=True, with_conv_shortcut=True)
+
+    x = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
+    x = Flatten()(x)
+
+    x = keras.layers.Dropout(0.5)(x)
+    regularSet = keras.regularizers.l1_l2(l1=0.01, l2=0.01)
+    x = keras.layers.Dense(8, activation="sigmoid", kernel_regularizer=regularSet)(x)
+
+    x = Dense(nb_classes)(x)
+    x = Activation('softmax')(x)
+
+    model = keras.models.Model(inputs=input_layer, outputs=x)
+    return model
+
+
+def resnet_CBAM_128ms(input_layer, nb_classes):
+    x = input_layer
+
+    x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='same')
+    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
+
+    x = identity_Block(x, nb_filter=64, kernel_size=(3, 3), strides=(1, 1), use_shortcut=True, with_conv_shortcut=True)
+    x = CbamBlock2D().layer(x)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2), use_shortcut=True, with_conv_shortcut=True)
+    x = CbamBlock2D().layer(x)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 2), use_shortcut=True, with_conv_shortcut=True)
+    x = CbamBlock2D().layer(x)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 1), use_shortcut=True, with_conv_shortcut=True)
+    x = CbamBlock2D().layer(x)
+
+    x = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
+    x = Flatten()(x)
+
+    x = keras.layers.Dropout(0.5)(x)
+    regularSet = keras.regularizers.l1_l2(l1=0.01, l2=0.01)
+    x = keras.layers.Dense(8, activation="relu", kernel_regularizer=regularSet)(x)
+
+    x = Dense(nb_classes)(x)
+    x = Activation('softmax')(x)
+
+    model = keras.models.Model(inputs=input_layer, outputs=x)
+    return model
+
+
+def plain_cnn_64ms(input_layer, nb_classes):
+    x = input_layer
+
+    x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='same')
+    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
+
+    x = identity_Block(x, nb_filter=64, kernel_size=(3, 3), strides=(1, 1))
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2))
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 2))
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 2))
+
+    x = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
+    x = Flatten()(x)
+
+    x = keras.layers.Dropout(0.5)(x)
+    regularSet = keras.regularizers.l1_l2(l1=0.01, l2=0.01)
+    x = keras.layers.Dense(8, activation="sigmoid", kernel_regularizer=regularSet)(x)
+
+    x = Dense(nb_classes)(x)
+    x = Activation('softmax')(x)
+
+    model = keras.models.Model(inputs=input_layer, outputs=x)
+    return model
+
+
+def resnet_64ms(input_layer, nb_classes):
+    x = input_layer
+
+    x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='same')
+    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
+
+    x = identity_Block(x, nb_filter=64, kernel_size=(3, 3), strides=(1, 1), use_shortcut=True, with_conv_shortcut=True)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2), use_shortcut=True, with_conv_shortcut=True)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 2), use_shortcut=True, with_conv_shortcut=True)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 2), use_shortcut=True, with_conv_shortcut=True)
+
+    x = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
+    x = Flatten()(x)
+
+    x = keras.layers.Dropout(0.5)(x)
+    regularSet = keras.regularizers.l1_l2(l1=0.01, l2=0.01)
+    x = keras.layers.Dense(8, activation="sigmoid", kernel_regularizer=regularSet)(x)
+
+    x = Dense(nb_classes)(x)
+    x = Activation('softmax')(x)
+
+    model = keras.models.Model(inputs=input_layer, outputs=x)
+    return model
+
+
+def resnet_CBAM_64ms(input_layer, nb_classes):
+    x = input_layer
+
+    x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='same')
+    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)
+
+    x = identity_Block(x, nb_filter=64, kernel_size=(3, 3), strides=(1, 1), use_shortcut=True, with_conv_shortcut=True)
+    x = CbamBlock2D().layer(x)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2), use_shortcut=True, with_conv_shortcut=True)
+    x = CbamBlock2D().layer(x)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 2), use_shortcut=True, with_conv_shortcut=True)
+    x = CbamBlock2D().layer(x)
+    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(1, 2), use_shortcut=True, with_conv_shortcut=True)
+    x = CbamBlock2D().layer(x)
+
+    x = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
+    x = Flatten()(x)
+
+    x = keras.layers.Dropout(0.5)(x)
+    regularSet = keras.regularizers.l1_l2(l1=0.01, l2=0.01)
+    x = keras.layers.Dense(8, activation="relu", kernel_regularizer=regularSet)(x)
+
+    x = Dense(nb_classes)(x)
+    x = Activation('softmax')(x)
+
+    model = keras.models.Model(inputs=input_layer, outputs=x)
+    return model
+
+
+class CbamBlock2D(object):
+    def __init__(self, reduction=8):
+        self.reduction = reduction
+
+    def layer(self, inputLayer):
+        inputLayer = self.channel_attention(inputLayer)
+        inputLayer = self.spacial_attention(inputLayer)
+
+        return inputLayer
+
+    def channel_attention(self, inputLayer):
+        channels = inputLayer.shape.as_list()[-1]
+        avg_x = GlobalAveragePooling2D()(inputLayer)
+        avg_x = Reshape((1, 1, channels))(avg_x)
+
+        max_x = GlobalMaxPooling2D()(inputLayer)
+        max_x = Reshape((1, 1, channels))(max_x)
+
+        Dense_One = Dense(units=int(channels) // self.reduction, activation='relu',
+                          kernel_initializer='he_normal', use_bias=True, bias_initializer='zeros')
+        Dense_Two = Dense(units=int(channels), activation='relu', kernel_initializer='he_normal',
+                          use_bias=True, bias_initializer='zeros')
+
+        avg_x = Dense_One(avg_x)
+        avg_x = Dense_Two(avg_x)
+
+        max_x = Dense_One(max_x)
+        max_x = Dense_Two(max_x)
+
+        cbam_feature = Add()([avg_x, max_x])
+        cbam_feature = Activation('sigmoid')(cbam_feature)
+
+        return multiply([inputLayer, cbam_feature])
+
+    def spacial_attention(self, input_feature):
+        kernel_size = 7
+
+        cbam_feature = input_feature
+
+        avg_pool = Lambda(lambda x: K.mean(x, axis=3, keepdims=True))(cbam_feature)
+        max_pool = Lambda(lambda x: K.max(x, axis=3, keepdims=True))(cbam_feature)
+        concat = Concatenate(axis=3)([avg_pool, max_pool])
+        cbam_feature = keras.layers.Conv2D(filters=1,
+                                           kernel_size=kernel_size,
+                                           strides=1,
+                                           padding='same',
+                                           activation='sigmoid',
+                                           use_bias=False, kernel_initializer='he_normal')(concat)
+
+        return multiply([input_feature, cbam_feature])
+
+
+def Conv2d_BN(x, nb_filter, kernel_size, strides=(1, 1), padding='same', name=None, active=True):
+    normal_func = InstanceNormalization
+
+    x = Conv2D(nb_filter, kernel_size, padding=padding, strides=strides, kernel_initializer="he_normal")(x)
+    x = normal_func()(x)
+    if active:
+        x = Activation('relu')(x)
+
+    #     x = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(x)
+    #     x = keras.layers.Dropout(0.2)(x)
+    return x
+
+
+def identity_Block(inpt, nb_filter, kernel_size, strides=(1, 1), use_shortcut=False, with_conv_shortcut=False):
+    if use_shortcut:
+        active = False
+    else:
+        active = True
+
+    x = Conv2d_BN(inpt, nb_filter=nb_filter, kernel_size=kernel_size, strides=strides, padding='same')
+    x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size, padding='same')
+    x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size, padding='same', active=active)
+
+    if use_shortcut:
+        if with_conv_shortcut:
+            shortcut = Conv2d_BN(inpt, nb_filter=nb_filter, strides=strides, kernel_size=kernel_size, active=active)
+            x = add([x, shortcut])
+            x = Activation('relu')(x)
+            return x
+        else:
+            x = add([x, inpt])
+            x = Activation('relu')(x)
+            return x
+    else:
+        return x
+
+
+def printMetrics(real, predict):
+    TP = np.where((predict == 1) & (real == 1))[0].size
+    FP = np.where((predict == 1) & (real == 0))[0].size
+    FN = np.where((predict == 0) & (real == 1))[0].size
+    TN = np.where((predict == 0) & (real == 0))[0].size
+
+    accu = round((TP + TN) * 100 / real.size, 6)
+    precision = round(TP * 100 / (TP + FP), 6)
+    recall = round(TP * 100 / (TP + FN), 6)
+
+    f1_score = round(2 * (precision * recall) / (precision + recall), 4)
+    print(f"accu:{accu}%；precision:{precision}%；recall:{recall}%；f1_score:{f1_score}%；")
+
+
+def train_model(model, train_x, train_y, val_x, val_y, trainEpochs, trainBatchSize,
+                verbose=2, outDir="/input/out/", binSize="64ms", modelName="cnn"):
+    from keras.callbacks import Callback
+    class LossHistory(Callback):
+
+        def on_train_begin(self, logs={}):
+            self.maxLoss = 0
+            self.maxAccu = 0
+            print('begin train')
+
+        def on_epoch_end(self, epoch, logs={}):
+            presentLoss = logs.get("val_loss")
+            presentAccu = logs.get("val_accuracy")
+
+            if presentAccu > self.maxAccu:
+                self.maxLoss = round(presentLoss, 4)
+                self.maxAccu = round(presentAccu, 4)
+
+                print("update max_loss:%s ;max_accu:%s" % (self.maxLoss, self.maxAccu))
+            else:
+                print("max_loss:%s ;max_accu:%s" % (self.maxLoss, self.maxAccu))
+
+        def on_train_end(self, logs={}):
+            print("max_loss:%s ;max_accu:%s" % (self.maxLoss, self.maxAccu))
+
+    reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.5, patience=20, min_lr=0.00005,
+                                                  verbose=1)
+
+    early_stopping = keras.callbacks.EarlyStopping(monitor="val_accuracy", patience=40, verbose=2)
+    if not os.path.exists(outDir):
+        os.makedirs(outDir)
+
+    saveBestModel = keras.callbacks.ModelCheckpoint(
+        f"{outDir}time_{binSize}_{modelName}_" + "{epoch:03d}_{val_accuracy:.4f}_{val_loss:.4f}.h5",
+        monitor='val_accuracy', verbose=1, save_best_only=True, save_weights_only=False, mode='max', period=1)
+    csvLogger = keras.callbacks.CSVLogger(outDir + "train.log", separator=',', append=False)
+
+    callbacks = [reduce_lr, early_stopping, saveBestModel, csvLogger, LossHistory()]
+
+    history = model.fit(train_x, train_y, epochs=trainEpochs, batch_size=trainBatchSize, verbose=verbose,
+                        validation_data=(val_x, val_y), callbacks=callbacks)
+
+    history_dict = history.history

code/test.py

+from keras.models import load_model
+from keras import backend as K
+from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization
+import numpy as np
+import model_utils
+import data_utils
+K.clear_session()
+
+# load the already trained model
+test_model=load_model(r"input/out_gpu/time_256ms_ResNet-CBAM256ms_013_0.9430_0.9751.h5",
+                      custom_objects={'InstanceNormalization': InstanceNormalization})
+
+
+# 64ms, 128ms, 256ms
+choonse_time_bin="256ms"
+data_set_dir=r"/workspace/binary_distinguish_GRB_by_DL-main/Binary_Distinguish_GRB_Datasetv1/data/dataset_256ms/"
+
+# load and pre-process data (train and validate)
+(test_x, test_y, test_info)=data_utils.get_test_data(data_set_dir,choonse_time_bin)
+# use the model to predict
+test_predict_y=test_model.predict(test_x)
+
+predict_class=np.argmax(test_predict_y,axis=1)
+real_class=np.argmax(test_y,axis=1)
+
+# output the: accuracy；precision；recall；f1_score
+model_utils.printMetrics(real_class, predict_class)
\ No newline at end of file

code/test_model.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# test the already trained model"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from keras.models import load_model\n",
+    "from keras import backend as K\n",
+    "from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization\n",
+    "import numpy as np\n",
+    "K.clear_session()\n",
+    "\n",
+    "# load the already trained model\n",
+    "test_model=load_model(r\"model_path\",\n",
+    "                      custom_objects={'InstanceNormalization': InstanceNormalization})"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import data_utils\n",
+    "\n",
+    "# 64ms, 128ms, 256ms\n",
+    "choonse_time_bin=\"64ms\"\n",
+    "data_set_dir=r\"\"\n",
+    "\n",
+    "# load and pre-process data (train and validate)\n",
+    "(test_x, test_y, test_info)=data_utils.get_test_data(data_set_dir,choonse_time_bin)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# use the model to predict\n",
+    "test_predict_y=test_model.predict(test_x)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import model_utils\n",
+    "\n",
+    "predict_class=np.argmax(test_predict_y,axis=1)\n",
+    "real_class=np.argmax(test_y,axis=1)\n",
+    "\n",
+    "# output the: accuracy；precision；recall；f1_score\n",
+    "model_utils.printMetrics(real_class, predict_class)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file

code/train.py

+import keras
+import tensorflow as tf
+import data_utils,model_utils
+
+
+config =  tf.compat.v1.ConfigProto()
+config.gpu_options.allow_growth = True
+session = tf.compat.v1.Session(config=config)
+# 64ms, 128ms, 256ms
+choonse_time_bin="256ms"
+# plain-CNN, ResNet, ResNet-CBAM
+choose_model="ResNet-CBAM"
+
+data_set_dir=r"/workspace/binary_distinguish_GRB_by_DL-main/Binary_Distinguish_GRB_Datasetv1/data/dataset_256ms/"
+# load and pre-process data (train and validate)
+(train_x, train_y, train_info), (val_x, val_y, val_info)=data_utils.get_train_val_data(data_set_dir,choonse_time_bin)
+
+trainBatchSize=32
+train_ds = tf.data.Dataset.from_tensor_slices((train_x, train_y))
+val_ds = tf.data.Dataset.from_tensor_slices((val_x, val_y))
+train_ds = train_ds.shuffle(20000).batch(trainBatchSize).prefetch(tf.data.AUTOTUNE)
+val_ds = val_ds.batch(trainBatchSize).prefetch(tf.data.AUTOTUNE)
+
+model_func_dic={
+    "plain-CNN-64ms": model_utils.plain_cnn_64ms,
+    "plain-CNN-128ms": model_utils.plain_cnn_128ms,
+    "plain-CNN-256ms": model_utils.plain_cnn_256ms,
+
+    "ResNet-64ms": model_utils.resnet_64ms,
+    "ResNet-128ms": model_utils.resnet_128ms,
+    "ResNet-256ms": model_utils.resnet_256ms,
+
+    "ResNet-CBAM-64ms": model_utils.resnet_CBAM_64ms,
+    "ResNet-CBAM-128ms": model_utils.resnet_CBAM_128ms,
+    "ResNet-CBAM-256ms": model_utils.resnet_CBAM_256ms,
+}
+model_func=model_func_dic.get(choose_model+"-"+choonse_time_bin)
+from keras import backend as K
+K.clear_session()
+
+input_shape, nb_classes = (train_x.shape[1:]), 2
+input_layer = keras.layers.Input(shape=input_shape, name='input')
+
+model = model_func(input_layer, nb_classes)
+model_name = choose_model+choonse_time_bin
+
+adam = keras.optimizers.Adam(lr=0.0001, beta_1=0.95, beta_2=0.999, epsilon=1e-08)
+model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
+model.summary()
+trainEpochs=20
+
+
+
+# model_utils.train_model(model, train_x, train_y, val_x, val_y, trainEpochs, trainBatchSize,modelName=model_name,outDir="gpuout/", binSize=choonse_time_bin)
+model.fit(
+    train_ds,
+    validation_data=val_ds,
+    epochs=trainEpochs,
+    )
+print("done")
\ No newline at end of file

code/train_backup.py

+import keras
+import tensorflow as tf
+import data_utils,model_utils
+
+config =  tf.compat.v1.ConfigProto()
+config.gpu_options.allow_growth = True
+session = tf.compat.v1.Session(config=config)
+# 64ms, 128ms, 256ms
+choonse_time_bin="256ms"
+# plain-CNN, ResNet, ResNet-CBAM
+choose_model="ResNet-CBAM"
+
+data_set_dir=r"/workspace/binary_distinguish_GRB_by_DL-main/Binary_Distinguish_GRB_Datasetv1/data/dataset_256ms/"
+# load and pre-process data (train and validate)
+(train_x, train_y, train_info), (val_x, val_y, val_info)=data_utils.get_train_val_data(data_set_dir,choonse_time_bin)
+
+model_func_dic={
+    "plain-CNN-64ms": model_utils.plain_cnn_64ms,
+    "plain-CNN-128ms": model_utils.plain_cnn_128ms,
+    "plain-CNN-256ms": model_utils.plain_cnn_256ms,
+
+    "ResNet-64ms": model_utils.resnet_64ms,
+    "ResNet-128ms": model_utils.resnet_128ms,
+    "ResNet-256ms": model_utils.resnet_256ms,
+
+    "ResNet-CBAM-64ms": model_utils.resnet_CBAM_64ms,
+    "ResNet-CBAM-128ms": model_utils.resnet_CBAM_128ms,
+    "ResNet-CBAM-256ms": model_utils.resnet_CBAM_256ms,
+}
+model_func=model_func_dic.get(choose_model+"-"+choonse_time_bin)
+from keras import backend as K
+K.clear_session()
+
+input_shape, nb_classes = (train_x.shape[1:]), 2
+input_layer = keras.layers.Input(shape=input_shape, name='input')
+
+model = model_func(input_layer, nb_classes)
+model_name = choose_model+choonse_time_bin
+
+adam = keras.optimizers.Adam(lr=0.0001, beta_1=0.95, beta_2=0.999, epsilon=1e-08)
+model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
+model.summary()
+trainEpochs=20
+trainBatchSize=32
+
+model_utils.train_model(model, train_x, train_y, val_x, val_y, trainEpochs, trainBatchSize,modelName=model_name,outDir="gpuout/", binSize=choonse_time_bin)
+print("done")
\ No newline at end of file

code/train_model.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# train_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import keras\n",
+    "import tensorflow as tf\n",
+    "import data_utils,model_utils\n",
+    "\n",
+    "config =  tf.compat.v1.ConfigProto()\n",
+    "config.gpu_options.allow_growth = True\n",
+    "session = tf.compat.v1.Session(config=config)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# 64ms, 128ms, 256ms\n",
+    "choonse_time_bin=\"64ms\"\n",
+    "# plain-CNN, ResNet, ResNet-CBAM\n",
+    "choose_model=\"plain-CNN\"\n",
+    "\n",
+    "data_set_dir=r\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# load and pre-process data (train and validate)\n",
+    "(train_x, train_y, train_info), (val_x, val_y, val_info)=data_utils.get_train_val_data(data_set_dir,choonse_time_bin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "model_func_dic={\n",
+    "    \"plain-CNN-64ms\": model_utils.plain_cnn_64ms,\n",
+    "    \"plain-CNN-128ms\": model_utils.plain_cnn_128ms,\n",
+    "    \"plain-CNN-256ms\": model_utils.plain_cnn_256ms,\n",
+    "\n",
+    "    \"ResNet-64ms\": model_utils.resnet_64ms,\n",
+    "    \"ResNet-128ms\": model_utils.resnet_128ms,\n",
+    "    \"ResNet-256ms\": model_utils.resnet_256ms,\n",
+    "\n",
+    "    \"ResNet-CBAM-64ms\": model_utils.resnet_CBAM_64ms,\n",
+    "    \"ResNet-CBAM-128ms\": model_utils.resnet_CBAM_128ms,\n",
+    "    \"ResNet-CBAM-256ms\": model_utils.resnet_CBAM_256ms,\n",
+    "}\n",
+    "model_func=model_func_dic.get(choose_model+\"-\"+choonse_time_bin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from keras import backend as K\n",
+    "K.clear_session()\n",
+    "\n",
+    "input_shape, nb_classes = (train_x.shape[1:]), 2\n",
+    "input_layer = keras.layers.Input(shape=input_shape, name='input')\n",
+    "\n",
+    "model = model_func(input_layer, nb_classes)\n",
+    "model_name = choose_model+choonse_time_bin\n",
+    "\n",
+    "adam = keras.optimizers.Adam(lr=0.0001, beta_1=0.95, beta_2=0.999, epsilon=1e-08)\n",
+    "model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "trainEpochs=1000\n",
+    "trainBatchSize=32\n",
+    "\n",
+    "model_utils.train_model(model, train_x, train_y, val_x, val_y, trainEpochs, trainBatchSize,modelName=model_name,outDir=\"input/out_gpu/\", binSize=choonse_time_bin)\n",
+    "print(\"done\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

gputest.py

+import tensorflow as tf
+
+# 检查 TensorFlow 版本
+print("TensorFlow 版本:", tf.__version__)
+
+# 检查是否有 GPU 可用
+physical_devices = tf.config.list_physical_devices('GPU')
+if len(physical_devices) > 0:
+    print("GPU 可用! 检测到的 GPU 设备:")
+    for device in physical_devices:
+        print(device)
+else:
+    print("未检测到 GPU，TensorFlow 将使用 CPU。")
+
+# 简单测试 GPU 是否工作
+try:
+    with tf.device('/GPU:0'):
+        a = tf.random.normal([1000, 1000])
+        b = tf.random.normal([1000, 1000])
+        c = tf.matmul(a, b)
+        print("GPU 上矩阵乘法成功！")
+except RuntimeError as e:
+    print("GPU 测试失败:", e)
\ No newline at end of file

tensorflow升级后部分code修改记录.txt

+#train
+1.code/train_model.ipynb
+config = tf.ConfigProto() -> config =  tf.compat.v1.ConfigProto()
+session = tf.Session(config=config) ->session = tf.compat.v1.Session(config=config)
+model_func=model_func_dic.get(choose_model+choonse_time_bin) -> model_func=model_func_dic.get(choose_model+"-"+choonse_time_bin)
+model_utils.train_model(model, train_x, train_y, val_x, val_y, trainEpochs, trainBatchSize,modelName=model_name,outDir="input/out/", binSize=choonse_time_bin)
+
+2.code/model_utils.py
+def train_model函数中的所有的val_acc ->val_accuracy
+
+
+
+
+

trained_model/keras_metadata.pb

+
+Һroot"_tf_keras_network*{"name": "model_1", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Functional", "config": {"name": "model_1", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 128, 1875, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input"}, "name": "input", "inbound_nodes": []}, {"class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [7, 7]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_1", "inbound_nodes": [[["input", 0, 0, {}]]]}, {"class_name": "InstanceNormalization", "config": {"name": "instance_normalization_1", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "instance_normalization_1", "inbound_nodes": [[["conv2d_1", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "activation_1", "trainable": true, "dtype": "float32", "activation": "relu"}, "name": "activation_1", "inbound_nodes": [[["instance_normalization_1", 0, 0, {}]]]}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [3, 3]}, "padding": "same", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_1", "inbound_nodes": [[["activation_1", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_2", "inbound_nodes": [[["max_pooling2d_1", 0, 0, {}]]]}, {"class_name": "InstanceNormalization", "config": {"name": "instance_normalization_2", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "instance_normalization_2", "inbound_nodes": [[["conv2d_2", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "activation_2", "trainable": true, "dtype": "float32", "activation": "relu"}, "name": "activation_2", "inbound_nodes": [[["instance_normalization_2", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_3", "inbound_nodes": [[["activation_2", 0, 0, {}]]]}, {"class_name": "InstanceNormalization", "config": {"name": "instance_normalization_3", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "instance_normalization_3", "inbound_nodes": [[["conv2d_3", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "activation_3", "trainable": true, "dtype": "float32", "activation": "relu"}, "name": "activation_3", "inbound_nodes": [[["instance_normalization_3", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_4", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_4", "inbound_nodes": [[["activation_3", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_5", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_5", "inbound_nodes": [[["max_pooling2d_1", 0, 0, {}]]]}, {"class_name": "InstanceNormalization", "config": {"name": "instance_normalization_4", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "instance_normalization_4", "inbound_nodes": [[["conv2d_4", 0, 0, {}]]]}, {"class_name": "InstanceNormalization", "config": {"name": "instance_normalization_5", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "instance_normalization_5", "inbound_nodes": [[["conv2d_5", 0, 0, {}]]]}, {"class_name": "Add", "config": {"name": "add_1", "trainable": true, "dtype": "float32"}, "name": "add_1", "inbound_nodes": [[["instance_normalization_4", 0, 0, {}], ["instance_normalization_5", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "activation_4", "trainable": true, "dtype": "float32", "activation": "relu"}, "name": "activation_4", "inbound_nodes": [[["add_1", 0, 0, {}]]]}, {"class_name": "GlobalAveragePooling2D", "config": {"name": "global_average_pooling2d_1", "trainable": true, "dtype": "float32", "data_format": "channels_last", "keepdims": false}, "name": "global_average_pooling2d_1", "inbound_nodes": [[["activation_4", 0, 0, {}]]]}, {"class_name": "GlobalMaxPooling2D", "config": {"name": "global_max_pooling2d_1", "trainable": true, "dtype": "float32", "data_format": "channels_last", "keepdims": false}, "name": "global_max_pooling2d_1", "inbound_nodes": [[["activation_4", 0, 0, {}]]]}, {"class_name": "Reshape", "config": {"name": "reshape_1", "trainable": true, "dtype": "float32", "target_shape": {"class_name": "__tuple__", "items": [1, 1, 64]}}, "name": "reshape_1", "inbound_nodes": [[["global_average_pooling2d_1", 0, 0, {}]]]}, {"class_name": "Reshape", "config": {"name": "reshape_2", "trainable": true, "dtype": "float32", "target_shape": {"class_name": "__tuple__", "items": [1, 1, 64]}}, "name": "reshape_2", "inbound_nodes": [[["global_max_pooling2d_1", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 8, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_1", "inbound_nodes": [[["reshape_1", 0, 0, {}]], [["reshape_2", 0, 0, {}]]]}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "dense_2", "inbound_nodes": [[["dense_1", 0, 0, {}]], [["dense_1", 1, 0, {}]]]}, {"class_name": "Add", "config": {"name": "add_2", "trainable": true, "dtype": "float32"}, "name": "add_2", "inbound_nodes": [[["dense_2", 0, 0, {}], ["dense_2", 1, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "activation_5", "trainable": true, "dtype": "float32", "activation": "sigmoid"}, "name": "activation_5", "inbound_nodes": [[["add_2", 0, 0, {}]]]}, {"class_name": "Multiply", "config": {"name": "multiply_1", "trainable": true, "dtype": "float32"}, "name": "multiply_1", "inbound_nodes": [[["activation_4", 0, 0, {}], ["activation_5", 0, 0, {}]]]}, {"class_name": "Lambda", "config": {"name": "lambda_1", "trainable": true, "dtype": "float32", "function": {"class_name": "__tuple__", "items": ["4wEAAAAAAAAAAQAAAAUAAABTAAAAcxAAAAB0AGoBfABkAWQCZAONA1MAKQRO6QMAAABUKQLaBGF4\naXPaCGtlZXBkaW1zKQLaAUvaBG1lYW4pAdoBeKkAcgcAAAB6NC9pbnB1dC9jb2RlL1BhcGFlcklk\nZW50aWZ5R3JiRm9yVHJhaW4vbGF5ZXJfdXRpbHMucHnaCDxsYW1iZGE+fQEAAPMAAAAA\n", null, null]}, "function_type": "lambda", "module": "keras.layers.core", "output_shape": null, "output_shape_type": "raw", "output_shape_module": null, "arguments": {}}, "name": "lambda_1", "inbound_nodes": [[["multiply_1", 0, 0, {}]]]}, {"class_name": "Lambda", "config": {"name": "lambda_2", "trainable": true, "dtype": "float32", "function": {"class_name": "__tuple__", "items": ["4wEAAAAAAAAAAQAAAAUAAABTAAAAcxAAAAB0AGoBfABkAWQCZAONA1MAKQRO6QMAAABUKQLaBGF4\naXPaCGtlZXBkaW1zKQLaAUvaA21heCkB2gF4qQByBwAAAHo0L2lucHV0L2NvZGUvUGFwYWVySWRl\nbnRpZnlHcmJGb3JUcmFpbi9sYXllcl91dGlscy5wedoIPGxhbWJkYT5+AQAA8wAAAAA=\n", null, null]}, "function_type": "lambda", "module": "keras.layers.core", "output_shape": null, "output_shape_type": "raw", "output_shape_module": null, "arguments": {}}, "name": "lambda_2", "inbound_nodes": [[["multiply_1", 0, 0, {}]]]}, {"class_name": "Concatenate", "config": {"name": "concatenate_1", "trainable": true, "dtype": "float32", "axis": 3}, "name": "concatenate_1", "inbound_nodes": [[["lambda_1", 0, 0, {}], ["lambda_2", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_6", "trainable": true, "dtype": "float32", "filters": 1, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "sigmoid", "use_bias": false, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_6", "inbound_nodes": [[["concatenate_1", 0, 0, {}]]]}, {"class_name": "Multiply", "config": {"name": "multiply_2", "trainable": true, "dtype": "float32"}, "name": "multiply_2", "inbound_nodes": [[["multiply_1", 0, 0, {}], ["conv2d_6", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_7", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_7", "inbound_nodes": [[["multiply_2", 0, 0, {}]]]}, {"class_name": "InstanceNormalization", "config": {"name": "instance_normalization_6", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "name": "instance_normalization_6", "inbound_nodes": [[["conv2d_7", 0, 0, {}]]]}, {"class_name": "Activation", "config": {"name": "activation_6", "trainable": true, "dtype": "float32", "activation": "relu"}, "name": "activation_6", "inbound_nodes": [[["instance_normalization_6", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_8", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_8", 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root.layer-0"_tf_keras_input_layer*{"class_name": "InputLayer", "name": "input", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 128, 1875, 1]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 128, 1875, 1]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input"}}2

+
+root.layer_with_weights-0"_tf_keras_layer*
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+root.layer_with_weights-1"_tf_keras_layer*{"name": "instance_normalization_1", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_1", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 4}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 5}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_1", 0, 0, {}]]], "shared_object_id": 6, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 216}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 64, 938, 64]}}2

+root.layer-3"_tf_keras_layer*{"name": "activation_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_1", "trainable": true, "dtype": "float32", "activation": "relu"}, "inbound_nodes": [[["instance_normalization_1", 0, 0, {}]]], "shared_object_id": 7}2

+root.layer-4"_tf_keras_layer*{"name": "max_pooling2d_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [3, 3]}, "padding": "same", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "inbound_nodes": [[["activation_1", 0, 0, {}]]], "shared_object_id": 8, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 217}}2

+
+root.layer_with_weights-2"_tf_keras_layer*
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+root.layer_with_weights-3"_tf_keras_layer*{"name": "instance_normalization_2", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_2", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 12}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 13}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_2", 0, 0, {}]]], "shared_object_id": 14, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 219}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 469, 64]}}2

+root.layer-7"_tf_keras_layer*{"name": "activation_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_2", "trainable": true, "dtype": "float32", "activation": "relu"}, "inbound_nodes": [[["instance_normalization_2", 0, 0, {}]]], "shared_object_id": 15}2

+
+	root.layer_with_weights-4"_tf_keras_layer*
+{"name": "conv2d_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 16}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 17}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["activation_2", 0, 0, {}]]], "shared_object_id": 18, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 64}}, "shared_object_id": 220}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 469, 64]}}2

+
+root.layer_with_weights-5"_tf_keras_layer*{"name": "instance_normalization_3", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_3", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 19}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 20}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_3", 0, 0, {}]]], "shared_object_id": 21, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 221}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 469, 64]}}2

+
root.layer-10"_tf_keras_layer*{"name": "activation_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_3", "trainable": true, "dtype": "float32", "activation": "relu"}, "inbound_nodes": [[["instance_normalization_3", 0, 0, {}]]], "shared_object_id": 22}2

+
+root.layer_with_weights-6"_tf_keras_layer*
+{"name": "conv2d_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_4", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 23}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 24}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["activation_3", 0, 0, {}]]], "shared_object_id": 25, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 64}}, "shared_object_id": 222}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 469, 64]}}2

+
+
root.layer_with_weights-7"_tf_keras_layer*
+{"name": "conv2d_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_5", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 26}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 27}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["max_pooling2d_1", 0, 0, {}]]], "shared_object_id": 28, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 64}}, "shared_object_id": 223}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 469, 64]}}2

+root.layer_with_weights-8"_tf_keras_layer*{"name": "instance_normalization_4", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_4", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 29}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 30}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_4", 0, 0, {}]]], "shared_object_id": 31, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 224}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 469, 64]}}2

+root.layer_with_weights-9"_tf_keras_layer*{"name": "instance_normalization_5", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_5", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 32}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 33}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_5", 0, 0, {}]]], "shared_object_id": 34, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 225}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 469, 64]}}2

+
root.layer-15"_tf_keras_layer*{"name": "add_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_1", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["instance_normalization_4", 0, 0, {}], ["instance_normalization_5", 0, 0, {}]]], "shared_object_id": 35, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 32, 469, 64]}, {"class_name": "TensorShape", "items": [null, 32, 469, 64]}]}2

+
root.layer-16"_tf_keras_layer*{"name": "activation_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_4", "trainable": true, "dtype": "float32", "activation": "relu"}, "inbound_nodes": [[["add_1", 0, 0, {}]]], "shared_object_id": 36}2

+
root.layer-17"_tf_keras_layer*{"name": "global_average_pooling2d_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "GlobalAveragePooling2D", "config": {"name": "global_average_pooling2d_1", "trainable": true, "dtype": "float32", "data_format": "channels_last", "keepdims": false}, "inbound_nodes": [[["activation_4", 0, 0, {}]]], "shared_object_id": 37, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 226}}2

+
root.layer-18"_tf_keras_layer*{"name": "global_max_pooling2d_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "GlobalMaxPooling2D", "config": {"name": "global_max_pooling2d_1", "trainable": true, "dtype": "float32", "data_format": "channels_last", "keepdims": false}, "inbound_nodes": [[["activation_4", 0, 0, {}]]], "shared_object_id": 38, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 227}}2

+
root.layer-19"_tf_keras_layer*{"name": "reshape_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Reshape", "config": {"name": "reshape_1", "trainable": true, "dtype": "float32", "target_shape": {"class_name": "__tuple__", "items": [1, 1, 64]}}, "inbound_nodes": [[["global_average_pooling2d_1", 0, 0, {}]]], "shared_object_id": 39}2

+
root.layer-20"_tf_keras_layer*{"name": "reshape_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Reshape", "config": {"name": "reshape_2", "trainable": true, "dtype": "float32", "target_shape": {"class_name": "__tuple__", "items": [1, 1, 64]}}, "inbound_nodes": [[["global_max_pooling2d_1", 0, 0, {}]]], "shared_object_id": 40}2

+root.layer_with_weights-10"_tf_keras_layer*{"name": "dense_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 8, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 41}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 42}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["reshape_1", 0, 0, {}]], [["reshape_2", 0, 0, {}]]], "shared_object_id": 43, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 64}}, "shared_object_id": 228}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 1, 64]}}2

+root.layer_with_weights-11"_tf_keras_layer*{"name": "dense_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 64, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 44}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 45}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_1", 0, 0, {}]], [["dense_1", 1, 0, {}]]], "shared_object_id": 46, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 8}}, "shared_object_id": 229}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 1, 8]}}2

+
root.layer-23"_tf_keras_layer*{"name": "add_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_2", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["dense_2", 0, 0, {}], ["dense_2", 1, 0, {}]]], "shared_object_id": 47, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 1, 1, 64]}, {"class_name": "TensorShape", "items": [null, 1, 1, 64]}]}2

+
root.layer-24"_tf_keras_layer*{"name": "activation_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_5", "trainable": true, "dtype": "float32", "activation": "sigmoid"}, "inbound_nodes": [[["add_2", 0, 0, {}]]], "shared_object_id": 48}2

+
root.layer-25"_tf_keras_layer*{"name": "multiply_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Multiply", "config": {"name": "multiply_1", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["activation_4", 0, 0, {}], ["activation_5", 0, 0, {}]]], "shared_object_id": 49, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 32, 469, 64]}, {"class_name": "TensorShape", "items": [null, 1, 1, 64]}]}2

+
root.layer-26"_tf_keras_layer*{"name": "lambda_1", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Lambda", "config": {"name": "lambda_1", "trainable": true, "dtype": "float32", "function": {"class_name": "__tuple__", "items": ["4wEAAAAAAAAAAQAAAAUAAABTAAAAcxAAAAB0AGoBfABkAWQCZAONA1MAKQRO6QMAAABUKQLaBGF4\naXPaCGtlZXBkaW1zKQLaAUvaBG1lYW4pAdoBeKkAcgcAAAB6NC9pbnB1dC9jb2RlL1BhcGFlcklk\nZW50aWZ5R3JiRm9yVHJhaW4vbGF5ZXJfdXRpbHMucHnaCDxsYW1iZGE+fQEAAPMAAAAA\n", null, null]}, "function_type": "lambda", "module": "keras.layers.core", "output_shape": null, "output_shape_type": "raw", "output_shape_module": null, "arguments": {}}, "inbound_nodes": [[["multiply_1", 0, 0, {}]]], "shared_object_id": 50}2

+
root.layer-27"_tf_keras_layer*{"name": "lambda_2", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Lambda", "config": {"name": "lambda_2", "trainable": true, "dtype": "float32", "function": {"class_name": "__tuple__", "items": ["4wEAAAAAAAAAAQAAAAUAAABTAAAAcxAAAAB0AGoBfABkAWQCZAONA1MAKQRO6QMAAABUKQLaBGF4\naXPaCGtlZXBkaW1zKQLaAUvaA21heCkB2gF4qQByBwAAAHo0L2lucHV0L2NvZGUvUGFwYWVySWRl\nbnRpZnlHcmJGb3JUcmFpbi9sYXllcl91dGlscy5wedoIPGxhbWJkYT5+AQAA8wAAAAA=\n", null, null]}, "function_type": "lambda", "module": "keras.layers.core", "output_shape": null, "output_shape_type": "raw", "output_shape_module": null, "arguments": {}}, "inbound_nodes": [[["multiply_1", 0, 0, {}]]], "shared_object_id": 51}2

+
root.layer-28"_tf_keras_layer*{"name": "concatenate_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Concatenate", "config": {"name": "concatenate_1", "trainable": true, "dtype": "float32", "axis": 3}, "inbound_nodes": [[["lambda_1", 0, 0, {}], ["lambda_2", 0, 0, {}]]], "shared_object_id": 52, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 32, 469, 1]}, {"class_name": "TensorShape", "items": [null, 32, 469, 1]}]}2

+
+root.layer_with_weights-12"_tf_keras_layer*
+{"name": "conv2d_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_6", "trainable": true, "dtype": "float32", "filters": 1, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "sigmoid", "use_bias": false, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 53}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 54}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["concatenate_1", 0, 0, {}]]], "shared_object_id": 55, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 2}}, "shared_object_id": 230}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 469, 2]}}2

+
root.layer-30"_tf_keras_layer*{"name": "multiply_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Multiply", "config": {"name": "multiply_2", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["multiply_1", 0, 0, {}], ["conv2d_6", 0, 0, {}]]], "shared_object_id": 56, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 32, 469, 64]}, {"class_name": "TensorShape", "items": [null, 32, 469, 1]}]}2

+
+ root.layer_with_weights-13"_tf_keras_layer*
+{"name": "conv2d_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_7", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 57}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 58}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["multiply_2", 0, 0, {}]]], "shared_object_id": 59, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 64}}, "shared_object_id": 231}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 469, 64]}}2

+!root.layer_with_weights-14"_tf_keras_layer*{"name": "instance_normalization_6", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_6", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 60}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 61}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_7", 0, 0, {}]]], "shared_object_id": 62, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 232}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 235, 128]}}2

+"
root.layer-33"_tf_keras_layer*{"name": "activation_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_6", "trainable": true, "dtype": "float32", "activation": "relu"}, "inbound_nodes": [[["instance_normalization_6", 0, 0, {}]]], "shared_object_id": 63}2

+
+#root.layer_with_weights-15"_tf_keras_layer*
+{"name": "conv2d_8", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_8", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 64}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 65}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["activation_6", 0, 0, {}]]], "shared_object_id": 66, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 128}}, "shared_object_id": 233}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 235, 128]}}2

+$root.layer_with_weights-16"_tf_keras_layer*{"name": "instance_normalization_7", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_7", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 67}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 68}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_8", 0, 0, {}]]], "shared_object_id": 69, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 234}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 235, 128]}}2

+%
root.layer-36"_tf_keras_layer*{"name": "activation_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_7", "trainable": true, "dtype": "float32", "activation": "relu"}, "inbound_nodes": [[["instance_normalization_7", 0, 0, {}]]], "shared_object_id": 70}2

+
+&root.layer_with_weights-17"_tf_keras_layer*
+{"name": "conv2d_9", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_9", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 71}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 72}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["activation_7", 0, 0, {}]]], "shared_object_id": 73, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 128}}, "shared_object_id": 235}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 235, 128]}}2

+
+'root.layer_with_weights-18"_tf_keras_layer*
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+(root.layer_with_weights-19"_tf_keras_layer*{"name": "instance_normalization_8", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_8", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 77}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 78}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_9", 0, 0, {}]]], "shared_object_id": 79, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 237}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 235, 128]}}2

+)root.layer_with_weights-20"_tf_keras_layer*{"name": "instance_normalization_9", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_9", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 80}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 81}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_10", 0, 0, {}]]], "shared_object_id": 82, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 238}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 235, 128]}}2

+*
root.layer-41"_tf_keras_layer*{"name": "add_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_3", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["instance_normalization_8", 0, 0, {}], ["instance_normalization_9", 0, 0, {}]]], "shared_object_id": 83, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 16, 235, 128]}, {"class_name": "TensorShape", "items": [null, 16, 235, 128]}]}2

++
root.layer-42"_tf_keras_layer*{"name": "activation_8", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_8", "trainable": true, "dtype": "float32", "activation": "relu"}, "inbound_nodes": [[["add_3", 0, 0, {}]]], "shared_object_id": 84}2

+,
root.layer-43"_tf_keras_layer*{"name": "global_average_pooling2d_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "GlobalAveragePooling2D", "config": {"name": "global_average_pooling2d_2", "trainable": true, "dtype": "float32", "data_format": "channels_last", "keepdims": false}, "inbound_nodes": [[["activation_8", 0, 0, {}]]], "shared_object_id": 85, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 239}}2

+-
root.layer-44"_tf_keras_layer*{"name": "global_max_pooling2d_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "GlobalMaxPooling2D", "config": {"name": "global_max_pooling2d_2", "trainable": true, "dtype": "float32", "data_format": "channels_last", "keepdims": false}, "inbound_nodes": [[["activation_8", 0, 0, {}]]], "shared_object_id": 86, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 240}}2

+.
root.layer-45"_tf_keras_layer*{"name": "reshape_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Reshape", "config": {"name": "reshape_3", "trainable": true, "dtype": "float32", "target_shape": {"class_name": "__tuple__", "items": [1, 1, 128]}}, "inbound_nodes": [[["global_average_pooling2d_2", 0, 0, {}]]], "shared_object_id": 87}2

+/
root.layer-46"_tf_keras_layer*{"name": "reshape_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Reshape", "config": {"name": "reshape_4", "trainable": true, "dtype": "float32", "target_shape": {"class_name": "__tuple__", "items": [1, 1, 128]}}, "inbound_nodes": [[["global_max_pooling2d_2", 0, 0, {}]]], "shared_object_id": 88}2

+0root.layer_with_weights-21"_tf_keras_layer*{"name": "dense_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "dtype": "float32", "units": 16, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 89}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 90}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["reshape_3", 0, 0, {}]], [["reshape_4", 0, 0, {}]]], "shared_object_id": 91, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 128}}, "shared_object_id": 241}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 1, 128]}}2

+1root.layer_with_weights-22"_tf_keras_layer*{"name": "dense_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 92}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 93}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_3", 0, 0, {}]], [["dense_3", 1, 0, {}]]], "shared_object_id": 94, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 16}}, "shared_object_id": 242}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 1, 16]}}2

+2
root.layer-49"_tf_keras_layer*{"name": "add_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_4", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["dense_4", 0, 0, {}], ["dense_4", 1, 0, {}]]], "shared_object_id": 95, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 1, 1, 128]}, {"class_name": "TensorShape", "items": [null, 1, 1, 128]}]}2

+3
root.layer-50"_tf_keras_layer*{"name": "activation_9", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_9", "trainable": true, "dtype": "float32", "activation": "sigmoid"}, "inbound_nodes": [[["add_4", 0, 0, {}]]], "shared_object_id": 96}2

+4
root.layer-51"_tf_keras_layer*{"name": "multiply_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Multiply", "config": {"name": "multiply_3", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["activation_8", 0, 0, {}], ["activation_9", 0, 0, {}]]], "shared_object_id": 97, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 16, 235, 128]}, {"class_name": "TensorShape", "items": [null, 1, 1, 128]}]}2

+5
root.layer-52"_tf_keras_layer*{"name": "lambda_3", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Lambda", "config": {"name": "lambda_3", "trainable": true, "dtype": "float32", "function": {"class_name": "__tuple__", "items": ["4wEAAAAAAAAAAQAAAAUAAABTAAAAcxAAAAB0AGoBfABkAWQCZAONA1MAKQRO6QMAAABUKQLaBGF4\naXPaCGtlZXBkaW1zKQLaAUvaBG1lYW4pAdoBeKkAcgcAAAB6NC9pbnB1dC9jb2RlL1BhcGFlcklk\nZW50aWZ5R3JiRm9yVHJhaW4vbGF5ZXJfdXRpbHMucHnaCDxsYW1iZGE+fQEAAPMAAAAA\n", null, null]}, "function_type": "lambda", "module": "keras.layers.core", "output_shape": null, "output_shape_type": "raw", "output_shape_module": null, "arguments": {}}, "inbound_nodes": [[["multiply_3", 0, 0, {}]]], "shared_object_id": 98}2

+6
root.layer-53"_tf_keras_layer*{"name": "lambda_4", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Lambda", "config": {"name": "lambda_4", "trainable": true, "dtype": "float32", "function": {"class_name": "__tuple__", "items": ["4wEAAAAAAAAAAQAAAAUAAABTAAAAcxAAAAB0AGoBfABkAWQCZAONA1MAKQRO6QMAAABUKQLaBGF4\naXPaCGtlZXBkaW1zKQLaAUvaA21heCkB2gF4qQByBwAAAHo0L2lucHV0L2NvZGUvUGFwYWVySWRl\nbnRpZnlHcmJGb3JUcmFpbi9sYXllcl91dGlscy5wedoIPGxhbWJkYT5+AQAA8wAAAAA=\n", null, null]}, "function_type": "lambda", "module": "keras.layers.core", "output_shape": null, "output_shape_type": "raw", "output_shape_module": null, "arguments": {}}, "inbound_nodes": [[["multiply_3", 0, 0, {}]]], "shared_object_id": 99}2

+7
root.layer-54"_tf_keras_layer*{"name": "concatenate_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Concatenate", "config": {"name": "concatenate_2", "trainable": true, "dtype": "float32", "axis": 3}, "inbound_nodes": [[["lambda_3", 0, 0, {}], ["lambda_4", 0, 0, {}]]], "shared_object_id": 100, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 16, 235, 1]}, {"class_name": "TensorShape", "items": [null, 16, 235, 1]}]}2

+
+8root.layer_with_weights-23"_tf_keras_layer*
+{"name": "conv2d_11", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_11", "trainable": true, "dtype": "float32", "filters": 1, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "sigmoid", "use_bias": false, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 101}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 102}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["concatenate_2", 0, 0, {}]]], "shared_object_id": 103, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 2}}, "shared_object_id": 243}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 235, 2]}}2

+9
root.layer-56"_tf_keras_layer*{"name": "multiply_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Multiply", "config": {"name": "multiply_4", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["multiply_3", 0, 0, {}], ["conv2d_11", 0, 0, {}]]], "shared_object_id": 104, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 16, 235, 128]}, {"class_name": "TensorShape", "items": [null, 16, 235, 1]}]}2

+
+:root.layer_with_weights-24"_tf_keras_layer*
+{"name": "conv2d_12", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_12", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 2]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 105}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 106}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["multiply_4", 0, 0, {}]]], "shared_object_id": 107, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 128}}, "shared_object_id": 244}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 235, 128]}}2

+;root.layer_with_weights-25"_tf_keras_layer*{"name": "instance_normalization_10", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_10", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 108}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 109}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_12", 0, 0, {}]]], "shared_object_id": 110, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 245}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 118, 128]}}2

+<
root.layer-59"_tf_keras_layer*{"name": "activation_10", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_10", "trainable": true, "dtype": "float32", "activation": "relu"}, "inbound_nodes": [[["instance_normalization_10", 0, 0, {}]]], "shared_object_id": 111}2

+
+=root.layer_with_weights-26"_tf_keras_layer*
+{"name": "conv2d_13", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_13", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 112}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 113}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["activation_10", 0, 0, {}]]], "shared_object_id": 114, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 128}}, "shared_object_id": 246}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 118, 128]}}2

+>root.layer_with_weights-27"_tf_keras_layer*{"name": "instance_normalization_11", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_11", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 115}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 116}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_13", 0, 0, {}]]], "shared_object_id": 117, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 247}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 118, 128]}}2

+?
root.layer-62"_tf_keras_layer*{"name": "activation_11", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_11", "trainable": true, "dtype": "float32", "activation": "relu"}, "inbound_nodes": [[["instance_normalization_11", 0, 0, {}]]], "shared_object_id": 118}2

+
+@root.layer_with_weights-28"_tf_keras_layer*
+{"name": "conv2d_14", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_14", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 119}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 120}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["activation_11", 0, 0, {}]]], "shared_object_id": 121, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 128}}, "shared_object_id": 248}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 118, 128]}}2

+
+Aroot.layer_with_weights-29"_tf_keras_layer*
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+D
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+E
root.layer-68"_tf_keras_layer*{"name": "activation_12", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_12", "trainable": true, "dtype": "float32", "activation": "relu"}, "inbound_nodes": [[["add_5", 0, 0, {}]]], "shared_object_id": 132}2

+F
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+G
root.layer-70"_tf_keras_layer*{"name": "global_max_pooling2d_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "GlobalMaxPooling2D", "config": {"name": "global_max_pooling2d_3", "trainable": true, "dtype": "float32", "data_format": "channels_last", "keepdims": false}, "inbound_nodes": [[["activation_12", 0, 0, {}]]], "shared_object_id": 134, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 253}}2

+H
root.layer-71"_tf_keras_layer*{"name": "reshape_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Reshape", "config": {"name": "reshape_5", "trainable": true, "dtype": "float32", "target_shape": {"class_name": "__tuple__", "items": [1, 1, 128]}}, "inbound_nodes": [[["global_average_pooling2d_3", 0, 0, {}]]], "shared_object_id": 135}2

+I
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+L
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+M
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+N
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+O
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+P
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+Q
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+
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+S
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+
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+V
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+
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+Y
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+
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+
+[root.layer_with_weights-40"_tf_keras_layer*
+{"name": "conv2d_20", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_20", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 2]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 170}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 171}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["multiply_6", 0, 0, {}]]], "shared_object_id": 172, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 128}}, "shared_object_id": 262}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 118, 128]}}2

+\root.layer_with_weights-41"_tf_keras_layer*{"name": "instance_normalization_16", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_16", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 173}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 174}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_19", 0, 0, {}]]], "shared_object_id": 175, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 263}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 59, 128]}}2

+]root.layer_with_weights-42"_tf_keras_layer*{"name": "instance_normalization_17", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "InstanceNormalization", "config": {"name": "instance_normalization_17", "trainable": true, "dtype": "float32", "axis": null, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 176}, "gamma_initializer": {"class_name": "Ones", "config": {}, "shared_object_id": 177}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv2d_20", 0, 0, {}]]], "shared_object_id": 178, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 264}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 59, 128]}}2

+^
root.layer-93"_tf_keras_layer*{"name": "add_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_7", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["instance_normalization_16", 0, 0, {}], ["instance_normalization_17", 0, 0, {}]]], "shared_object_id": 179, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 16, 59, 128]}, {"class_name": "TensorShape", "items": [null, 16, 59, 128]}]}2

+_
root.layer-94"_tf_keras_layer*{"name": "activation_16", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_16", "trainable": true, "dtype": "float32", "activation": "relu"}, "inbound_nodes": [[["add_7", 0, 0, {}]]], "shared_object_id": 180}2

+`
root.layer-95"_tf_keras_layer*{"name": "global_average_pooling2d_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "GlobalAveragePooling2D", "config": {"name": "global_average_pooling2d_4", "trainable": true, "dtype": "float32", "data_format": "channels_last", "keepdims": false}, "inbound_nodes": [[["activation_16", 0, 0, {}]]], "shared_object_id": 181, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 265}}2

+a
root.layer-96"_tf_keras_layer*{"name": "global_max_pooling2d_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "GlobalMaxPooling2D", "config": {"name": "global_max_pooling2d_4", "trainable": true, "dtype": "float32", "data_format": "channels_last", "keepdims": false}, "inbound_nodes": [[["activation_16", 0, 0, {}]]], "shared_object_id": 182, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 266}}2

+b
root.layer-97"_tf_keras_layer*{"name": "reshape_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Reshape", "config": {"name": "reshape_7", "trainable": true, "dtype": "float32", "target_shape": {"class_name": "__tuple__", "items": [1, 1, 128]}}, "inbound_nodes": [[["global_average_pooling2d_4", 0, 0, {}]]], "shared_object_id": 183}2

+c
root.layer-98"_tf_keras_layer*{"name": "reshape_8", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Reshape", "config": {"name": "reshape_8", "trainable": true, "dtype": "float32", "target_shape": {"class_name": "__tuple__", "items": [1, 1, 128]}}, "inbound_nodes": [[["global_max_pooling2d_4", 0, 0, {}]]], "shared_object_id": 184}2

+droot.layer_with_weights-43"_tf_keras_layer*{"name": "dense_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_7", "trainable": true, "dtype": "float32", "units": 16, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 185}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 186}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["reshape_7", 0, 0, {}]], [["reshape_8", 0, 0, {}]]], "shared_object_id": 187, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 128}}, "shared_object_id": 267}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 1, 128]}}2

+eroot.layer_with_weights-44"_tf_keras_layer*{"name": "dense_8", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_8", "trainable": true, "dtype": "float32", "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 188}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 189}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_7", 0, 0, {}]], [["dense_7", 1, 0, {}]]], "shared_object_id": 190, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 16}}, "shared_object_id": 268}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 1, 1, 16]}}2

+froot.layer-101"_tf_keras_layer*{"name": "add_8", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Add", "config": {"name": "add_8", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["dense_8", 0, 0, {}], ["dense_8", 1, 0, {}]]], "shared_object_id": 191, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 1, 1, 128]}, {"class_name": "TensorShape", "items": [null, 1, 1, 128]}]}2

+groot.layer-102"_tf_keras_layer*{"name": "activation_17", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_17", "trainable": true, "dtype": "float32", "activation": "sigmoid"}, "inbound_nodes": [[["add_8", 0, 0, {}]]], "shared_object_id": 192}2

+hroot.layer-103"_tf_keras_layer*{"name": "multiply_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Multiply", "config": {"name": "multiply_7", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["activation_16", 0, 0, {}], ["activation_17", 0, 0, {}]]], "shared_object_id": 193, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 16, 59, 128]}, {"class_name": "TensorShape", "items": [null, 1, 1, 128]}]}2

+iroot.layer-104"_tf_keras_layer*{"name": "lambda_7", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Lambda", "config": {"name": "lambda_7", "trainable": true, "dtype": "float32", "function": {"class_name": "__tuple__", "items": ["4wEAAAAAAAAAAQAAAAUAAABTAAAAcxAAAAB0AGoBfABkAWQCZAONA1MAKQRO6QMAAABUKQLaBGF4\naXPaCGtlZXBkaW1zKQLaAUvaBG1lYW4pAdoBeKkAcgcAAAB6NC9pbnB1dC9jb2RlL1BhcGFlcklk\nZW50aWZ5R3JiRm9yVHJhaW4vbGF5ZXJfdXRpbHMucHnaCDxsYW1iZGE+fQEAAPMAAAAA\n", null, null]}, "function_type": "lambda", "module": "keras.layers.core", "output_shape": null, "output_shape_type": "raw", "output_shape_module": null, "arguments": {}}, "inbound_nodes": [[["multiply_7", 0, 0, {}]]], "shared_object_id": 194}2

+jroot.layer-105"_tf_keras_layer*{"name": "lambda_8", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Lambda", "config": {"name": "lambda_8", "trainable": true, "dtype": "float32", "function": {"class_name": "__tuple__", "items": ["4wEAAAAAAAAAAQAAAAUAAABTAAAAcxAAAAB0AGoBfABkAWQCZAONA1MAKQRO6QMAAABUKQLaBGF4\naXPaCGtlZXBkaW1zKQLaAUvaA21heCkB2gF4qQByBwAAAHo0L2lucHV0L2NvZGUvUGFwYWVySWRl\nbnRpZnlHcmJGb3JUcmFpbi9sYXllcl91dGlscy5wedoIPGxhbWJkYT5+AQAA8wAAAAA=\n", null, null]}, "function_type": "lambda", "module": "keras.layers.core", "output_shape": null, "output_shape_type": "raw", "output_shape_module": null, "arguments": {}}, "inbound_nodes": [[["multiply_7", 0, 0, {}]]], "shared_object_id": 195}2

+kroot.layer-106"_tf_keras_layer*{"name": "concatenate_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Concatenate", "config": {"name": "concatenate_4", "trainable": true, "dtype": "float32", "axis": 3}, "inbound_nodes": [[["lambda_7", 0, 0, {}], ["lambda_8", 0, 0, {}]]], "shared_object_id": 196, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 16, 59, 1]}, {"class_name": "TensorShape", "items": [null, 16, 59, 1]}]}2

+
+lroot.layer_with_weights-45"_tf_keras_layer*
+{"name": "conv2d_21", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_21", "trainable": true, "dtype": "float32", "filters": 1, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "sigmoid", "use_bias": false, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 2.0, "mode": "fan_in", "distribution": "truncated_normal", "seed": null}, "shared_object_id": 197}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 198}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["concatenate_4", 0, 0, {}]]], "shared_object_id": 199, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 2}}, "shared_object_id": 269}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 59, 2]}}2

+mroot.layer-108"_tf_keras_layer*{"name": "multiply_8", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Multiply", "config": {"name": "multiply_8", "trainable": true, "dtype": "float32"}, "inbound_nodes": [[["multiply_7", 0, 0, {}], ["conv2d_21", 0, 0, {}]]], "shared_object_id": 200, "build_input_shape": [{"class_name": "TensorShape", "items": [null, 16, 59, 128]}, {"class_name": "TensorShape", "items": [null, 16, 59, 1]}]}2

+nroot.layer-109"_tf_keras_layer*{"name": "average_pooling2d_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "AveragePooling2D", "config": {"name": "average_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [3, 3]}, "padding": "same", "strides": {"class_name": "__tuple__", "items": [1, 1]}, "data_format": "channels_last"}, "inbound_nodes": [[["multiply_8", 0, 0, {}]]], "shared_object_id": 201, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 270}}2

+oroot.layer-110"_tf_keras_layer*{"name": "flatten_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "inbound_nodes": [[["average_pooling2d_1", 0, 0, {}]]], "shared_object_id": 202, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 271}}2

+proot.layer-111"_tf_keras_layer*{"name": "dropout_1", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dropout", "config": {"name": "dropout_1", "trainable": true, "dtype": "float32", "rate": 0.5, "noise_shape": null, "seed": null}, "inbound_nodes": [[["flatten_1", 0, 0, {}]]], "shared_object_id": 203}2

+	qroot.layer_with_weights-46"_tf_keras_layer*{"name": "dense_9", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_9", "trainable": true, "dtype": "float32", "units": 8, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}, "shared_object_id": 204}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 205}, "kernel_regularizer": {"class_name": "L1L2", "config": {"l1": 0.009999999776482582, "l2": 0.009999999776482582}, "shared_object_id": 206}, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dropout_1", 0, 0, {}]]], "shared_object_id": 207, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 120832}}, "shared_object_id": 272}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 120832]}}2

+rroot.layer_with_weights-47"_tf_keras_layer*{"name": "dense_10", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_10", "trainable": true, "dtype": "float32", "units": 2, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}, "shared_object_id": 208}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 209}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["dense_9", 0, 0, {}]]], "shared_object_id": 210, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 8}}, "shared_object_id": 273}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 8]}}2

+sroot.layer-114"_tf_keras_layer*{"name": "activation_18", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Activation", "config": {"name": "activation_18", "trainable": true, "dtype": "float32", "activation": "softmax"}, "inbound_nodes": [[["dense_10", 0, 0, {}]]], "shared_object_id": 211}2

+
	root.keras_api.metrics.0"_tf_keras_metric*
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 274}2

+
	root.keras_api.metrics.1"_tf_keras_metric*
{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "categorical_accuracy"}, "shared_object_id": 214}2

\ No newline at end of file