"""A deep MNIST classifier using convolutional layers.""" import logging import math import tempfile import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import nni FLAGS = None logger = logging.getLogger('mnist_AutoML') class MnistNetwork(object): ''' MnistNetwork is for initlizing and building basic network for mnist. ''' def __init__(self, channel_1_num, channel_2_num, conv_size, hidden_size, pool_size, learning_rate, x_dim=784, y_dim=10): self.channel_1_num = channel_1_num self.channel_2_num = channel_2_num self.conv_size = conv_size self.hidden_size = hidden_size self.pool_size = pool_size self.learning_rate = learning_rate self.x_dim = x_dim self.y_dim = y_dim self.images = tf.placeholder(tf.float32, [None, self.x_dim], name='input_x') self.labels = tf.placeholder(tf.float32, [None, self.y_dim], name='input_y') self.keep_prob = tf.placeholder(tf.float32, name='keep_prob') self.train_step = None self.accuracy = None def build_network(self): ''' Building network for mnist ''' # Reshape to use within a convolutional neural net. # Last dimension is for "features" - there is only one here, since images are # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. with tf.name_scope('reshape'): try: input_dim = int(math.sqrt(self.x_dim)) except: print( 'input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim)) logger.debug( 'input dim cannot be sqrt and reshape. input dim: %s', str(self.x_dim)) raise x_image = tf.reshape(self.images, [-1, input_dim, input_dim, 1]) # First convolutional layer - maps one grayscale image to 32 feature maps. with tf.name_scope('conv1'): w_conv1 = weight_variable( [self.conv_size, self.conv_size, 1, self.channel_1_num]) b_conv1 = bias_variable([self.channel_1_num]) h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1) # Pooling layer - downsamples by 2X. with tf.name_scope('pool1'): h_pool1 = max_pool(h_conv1, self.pool_size) # Second convolutional layer -- maps 32 feature maps to 64. with tf.name_scope('conv2'): w_conv2 = weight_variable([self.conv_size, self.conv_size, self.channel_1_num, self.channel_2_num]) b_conv2 = bias_variable([self.channel_2_num]) h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2) # Second pooling layer. with tf.name_scope('pool2'): h_pool2 = max_pool(h_conv2, self.pool_size) # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image # is down to 7x7x64 feature maps -- maps this to 1024 features. last_dim = int(input_dim / (self.pool_size * self.pool_size)) with tf.name_scope('fc1'): w_fc1 = weight_variable( [last_dim * last_dim * self.channel_2_num, self.hidden_size]) b_fc1 = bias_variable([self.hidden_size]) h_pool2_flat = tf.reshape( h_pool2, [-1, last_dim * last_dim * self.channel_2_num]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1) # Dropout - controls the complexity of the model, prevents co-adaptation of features. with tf.name_scope('dropout'): h_fc1_drop = tf.nn.dropout(h_fc1, self.keep_prob) # Map the 1024 features to 10 classes, one for each digit with tf.name_scope('fc2'): w_fc2 = weight_variable([self.hidden_size, self.y_dim]) b_fc2 = bias_variable([self.y_dim]) y_conv = tf.matmul(h_fc1_drop, w_fc2) + b_fc2 with tf.name_scope('loss'): cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=self.labels, logits=y_conv)) with tf.name_scope('adam_optimizer'): self.train_step = tf.train.AdamOptimizer( self.learning_rate).minimize(cross_entropy) with tf.name_scope('accuracy'): correct_prediction = tf.equal( tf.argmax(y_conv, 1), tf.argmax(self.labels, 1)) self.accuracy = tf.reduce_mean( tf.cast(correct_prediction, tf.float32)) def conv2d(x_input, w_matrix): """conv2d returns a 2d convolution layer with full stride.""" return tf.nn.conv2d(x_input, w_matrix, strides=[1, 1, 1, 1], padding='SAME') def max_pool(x_input, pool_size): """max_pool downsamples a feature map by 2X.""" return tf.nn.max_pool(x_input, ksize=[1, pool_size, pool_size, 1], strides=[1, pool_size, pool_size, 1], padding='SAME') def weight_variable(shape): """weight_variable generates a weight variable of a given shape.""" initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): """bias_variable generates a bias variable of a given shape.""" initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def main(params): ''' Main function, build mnist network, run and send result to NNI. ''' # Import data mnist = input_data.read_data_sets(params['data_dir'], one_hot=True) print('Mnist download data down.') logger.debug('Mnist download data down.') # Create the model # Build the graph for the deep net mnist_network = MnistNetwork(channel_1_num=params['channel_1_num'], channel_2_num=params['channel_2_num'], conv_size=params['conv_size'], hidden_size=params['hidden_size'], pool_size=params['pool_size'], learning_rate=params['learning_rate']) mnist_network.build_network() logger.debug('Mnist build network done.') # Write log graph_location = tempfile.mkdtemp() logger.debug('Saving graph to: %s', graph_location) train_writer = tf.summary.FileWriter(graph_location) train_writer.add_graph(tf.get_default_graph()) test_acc = 0.0 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(params['batch_num']): batch = mnist.train.next_batch(params['batch_size']) mnist_network.train_step.run(feed_dict={mnist_network.images: batch[0], mnist_network.labels: batch[1], mnist_network.keep_prob: 1 - params['dropout_rate']} ) if i % 100 == 0: test_acc = mnist_network.accuracy.eval( feed_dict={mnist_network.images: mnist.test.images, mnist_network.labels: mnist.test.labels, mnist_network.keep_prob: 1.0}) nni.report_intermediate_result(test_acc) logger.debug('test accuracy %g', test_acc) logger.debug('Pipe send intermediate result done.') test_acc = mnist_network.accuracy.eval( feed_dict={mnist_network.images: mnist.test.images, mnist_network.labels: mnist.test.labels, mnist_network.keep_prob: 1.0}) nni.report_final_result(test_acc) logger.debug('Final result is %g', test_acc) logger.debug('Send final result done.') def generate_default_params(): ''' Generate default parameters for mnist network. ''' params = { 'data_dir': '/tmp/tensorflow/mnist/input_data', 'dropout_rate': 0.5, 'channel_1_num': 32, 'channel_2_num': 64, 'conv_size': 5, 'pool_size': 2, 'hidden_size': 1024, 'learning_rate': 1e-4, 'batch_num': 2000, 'batch_size': 32} return params if __name__ == '__main__': try: # get parameters form tuner RCV_PARAMS = nni.get_next_parameter() logger.debug(RCV_PARAMS) # run params = generate_default_params() params.update(RCV_PARAMS) main(params) except Exception as exception: logger.exception(exception) raise