def build_lstm_model(number_neurons_per_layer, l2_lambda, input_size, number_layers, dropout):
test_model = tf_analaysis.nn_model()
regularizer = regularizers.l2(l2_lambda)
model_structure = [({'units': 10, 'input_shape': (None, input_size),
'return_sequences': True}, 'LSTM')]
for layer_idx in range(number_layers):
model_structure.append({'units': number_neurons_per_layer, 'activation': tf.nn.relu,
'kernel_regularizer': regularizer})
model_structure.append(({'rate': dropout}, 'dropout'))
model_structure.append({'units': 4, 'kernel_regularizer': regularizer})
test_model.build_nn_model(hidden_layer_structure=model_structure)
return test_model
def build_fnn_model(number_neurons_per_layer, l2_lambda, input_size, number_layers, dropout):
# K.get_session().close()
# K.set_session(tf.Session())
test_model = tf_analaysis.nn_model()
regularizer = regularizers.l2(l2_lambda)
# model_structure = [({'units': input_size, 'input_shape': (input_size), 'activation': tf.nn.relu, 'kernel_regularizer': regularizer}, 'dense')]
model_structure = [{'units': input_size, 'activation': tf.nn.relu, 'kernel_regularizer': regularizer, 'input_dim': input_size}]
for layer_idx in range(number_layers):
model_structure.append(
{'units': number_neurons_per_layer, 'activation': tf.nn.relu, 'kernel_regularizer': regularizer})
model_structure.append(({'rate': dropout}, 'dropout'))
model_structure.append({'units': 4, 'kernel_regularizer': regularizer})
test_model.build_nn_model(hidden_layer_structure=model_structure)
# K.get_session().run(tf.global_variables_initializer())
# test_model.model.fit()
return test_model
y_test = pd.Series(y.iloc[i], [y.index[i]])
X_train = X.drop(X.index[i])
y_train = y.drop(y.index[i])
if USE_SIMILARITY:
X_train = X_train.append(X_train_censored)
y_train = y_train.append(y_train_censored)
# shuffle
idx = np.random.permutation(X_train.index)
X_train = X_train.reindex(idx)
y_train = y_train.reindex(idx)
algo_name = 'Neural Network'
test_model = tf_analaysis.nn_model()
regularizer = regularizers.l2(l2_lambda)
test_model.build_nn_model(hidden_layer_structure=[
{
'units': n_components,
'activation': tf.nn.relu,
'kernel_regularizer': regularizer
}, {
'units': 50,
'activation': tf.nn.relu,
'kernel_regularizer': regularizer
}, ({
'rate': dropput
}, 'dropout'), {
'units': 20,
'activation': tf.nn.relu,