Created
February 8, 2018 19:25
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A stateful LSTM Model for sequential leaarning
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| import os | |
| import keras | |
| from keras import Input | |
| from keras.layers import BatchNormalization, Dense, LSTM, concatenate, Dropout, TimeDistributed | |
| from keras.optimizers import RMSprop | |
| from keras.utils import plot_model | |
| from metrics import in_top5, top5_accuracy | |
| def gen_nn_model(hyperparameters, target_symbols): | |
| # - Input definitions: | |
| batch_size = hyperparameters['batch_size'] | |
| n_cols = hyperparameters['n_cols'] | |
| n_history = hyperparameters['n_history'] | |
| # -- Daily Input | |
| daily_input = Input(shape = (n_history, n_cols), batch_shape = (batch_size, n_history, n_cols), dtype = 'float32', | |
| name = 'daily_input') | |
| normalized_daily_input = BatchNormalization()(daily_input) | |
| # normalized_daily_input = TimeDistributed(Dense(n_cols, activation='linear'))(normalized_daily_input) | |
| normalized_daily_input = TimeDistributed(Dense(n_cols, activation = 'relu'))(normalized_daily_input) | |
| normalized_daily_input = TimeDistributed(Dense(n_cols, activation = 'relu'))(normalized_daily_input) | |
| normalized_daily_input = TimeDistributed(Dense(n_cols, activation = 'relu'))(normalized_daily_input) | |
| normalized_daily_input = Dropout(0.2)(normalized_daily_input) | |
| # Deep LSTM Layers | |
| x = LSTM(n_cols // 7, | |
| return_state = False, | |
| return_sequences = True, | |
| stateful = True)(normalized_daily_input) | |
| x = LSTM(n_cols // 7, | |
| stateful = False)(x) | |
| r = Dense(n_cols - 1, activation = 'tanh')(x) | |
| r = Dense(n_cols - 1, activation = 'tanh')(r) | |
| r = Dense(len(target_symbols), activation = 'linear', name = 'r')(r) | |
| joint_flow = concatenate([x, r]) | |
| p = Dense(n_cols - 1, activation = 'sigmoid')(joint_flow) | |
| p = Dense(n_cols - 1, activation = 'sigmoid')(p) | |
| # p = keras.layers.core.Lambda(lambda x: x *5)(p) | |
| p = Dense(len(target_symbols), activation = 'softmax', name = 'p')(p) | |
| # l = keras.layers.core.Lambda(lambda x: negative(x))(p) | |
| l = Dense(n_cols - 1, activation = 'sigmoid')(joint_flow) | |
| l = Dense(n_cols - 1, activation = 'sigmoid')(l) | |
| # l = keras.layers.core.Lambda(lambda x: x / 2)(l) | |
| l = Dense(len(target_symbols), activation = 'softmax', name = 'l')(l) | |
| # -- Model creation | |
| inputs = [daily_input] | |
| outputs = [r, p, l] | |
| model = keras.models.Model(inputs = inputs, outputs = outputs) | |
| return batch_size, model | |
| def compile_model(model): | |
| lr = 0.1 | |
| loss = { 'r': 'mean_absolute_error', | |
| 'p': 'kullback_leibler_divergence', | |
| 'l': 'kullback_leibler_divergence' | |
| } | |
| loss_weights = { 'r': .20, | |
| 'p': .40, | |
| 'l': .40, | |
| } | |
| opt = RMSprop(lr = lr) | |
| # opt = 'adam' | |
| metrics = { 'p': [in_top5, top5_accuracy], | |
| 'l': [in_top5, top5_accuracy] } | |
| model.compile(optimizer = opt, | |
| loss = loss, | |
| loss_weights = loss_weights, | |
| metrics = metrics | |
| ) | |
| return model | |
| def show_model_details(EXP_NAME, EXP_PATH, model): | |
| print(model.summary()) | |
| str_image_file_name = os.path.join(EXP_PATH, '{0}.png'.format(EXP_NAME)) | |
| plot_model(model, to_file = str_image_file_name, | |
| show_shapes = True, | |
| show_layer_names = True, | |
| rankdir = 'TD') |
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