import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, InputLayer, Dropout, Conv1D, Conv2D, Flatten, Reshape, MaxPooling1D, MaxPooling2D, BatchNormalization, TimeDistributed, DepthwiseConv1D, Reshape from tensorflow.keras.optimizers import Adam EPOCHS = 500 # this controls the batch size, or you can manipulate the tf.data.Dataset objects yourself BATCH_SIZE = 32 train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False) validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False) # model architecture model = Sequential() # # Middle layers # model.add(Reshape(target_shape=(20, 9), # input_shape=(180,))) # model.add(DepthwiseConv1D(3, # kernel size # strides=1, # padding='same', # depth_multiplier=1, # data_format='channels_last', # activation='relu')) # # Flatten and DNN for classification # model.add(Flatten()) # model.add(Dropout(0.25)) # model.add(Dense(80, # activation=tf.keras.activations.relu)) # model.add(Dropout(0.25)) model.add(Dense(80, activation='relu')) model.add(Dropout(0.25)) model.add(Dense(40, activation='relu')) model.add(Dropout(0.25)) # Final model (for regression) model.add(Dense(classes, name='y_pred', activation='linear')) # this controls the learning rate opt = Adam(learning_rate=0.005, beta_1=0.9, beta_2=0.999) callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count, epochs=EPOCHS)) # train the neural network model.compile(loss='mean_squared_error', optimizer=opt) model.fit(train_dataset, epochs=EPOCHS, validation_data=validation_dataset, verbose=2, callbacks=callbacks) # Use this flag to disable per-channel quantization for a model. # This can reduce RAM usage for convolutional models, but may have # an impact on accuracy. disable_per_channel_quantization = False