Delete train_backup.py

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")
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