print(panier)
print(X1)
X = X1[:, :-1, :]
Y = X1[:, -1, :]
#print(X.shape)
#print(Y.shape)
#Train test split to train and test model
x_train, x_test, y_train, y_test = train_test_split(X, Y)
#model
model_instacart = LSTM_model(X.shape[1], X.shape[2])
model_instacart.summary()
callback_1 = keras.callbacks.TensorBoard(log_dir='trainings/t2')
callback_2 = keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=0.0005,
patience=5)
callback_3 = keras.callbacks.ModelCheckpoint(filepath='weights.hdf5',
verbose=1,
save_best_only=True)
model_instacart.fit(x_train,
y_train,
epochs=50,
batch_size=256,
verbose=1,
callbacks=[callback_1, callback_2, callback_3])
with open(val_feature_path, 'rb') as file:
val_feature_data = pickle.load(file)
file.close()
labels = model_config.labels
train_feature_generator = feature_generator(
train_feature_data,
labels=labels,
batch_size=feature_batch_size,
clip_feature_shape=clip_feature_size)
val_feature_generator = feature_generator(val_feature_data,
labels,
batch_size=feature_batch_size,
clip_feature_shape=clip_feature_size)
# LSTM model
lstmModel = LSTM_model(model_config.lstm_step)
lstmModel.summary()
lstmModel._get_distribution_strategy = lambda: None
lstmModel.compile(optimizer=adam(lr=0.001, decay=1e-6),
loss='categorical_crossentropy',
metrics=['accuracy'])
# lstm train
lstmModel_history = lstmModel.fit_generator(
train_feature_generator,
steps_per_epoch=4,
validation_data=val_feature_generator,
validation_steps=1,
epochs=64,
callbacks=callback_list)
# lstmModel.save('./out/lstm_weight_final_2.h5')
# with open('./out/lstmModel_history_2.pkl', 'wb') as file: