# x_train = x_train.reshape(x_train.shape[0], input_shape) # x_test = x_test.reshape(x_test.shape[0], input_shape) # Converting the datatypes as accepted by keras x_train = x_train.astype('float32') y_train = y_train.astype('float32') # One hot encoding of the labels print("One Hot Encoding of the labels...\n\n") y_train = to_categorical(y_train, num_classes) y_test = to_categorical(y_test, num_classes) print("Success!!!\n\n") # Creating the model print("Defining the model...\n\n") model = obj.create_model(input_shape, num_classes) # List of callback actions that are to be taken to stop the training after the model reaches an optimal point callbacks_list = [ tf.keras.callbacks.ModelCheckpoint(filepath='./models/max_features.h5', monitor='val_loss', save_best_only=True) # ), # tf.keras.callbacks.EarlyStopping(monitor = 'acc', patience = 1) ] # Compiling and fitting the model print("Now fitting the model and starting with the training...\n\n") compile_fit(model, callbacks_list, x_train, y_train, x_test, y_test) # ============================================================================================