TIME_STEPS,
INPUT_DIM,
))
attention_mul = attention_3d_block(inputs)
lstm_units = 32
attention_mul = LSTM(lstm_units, return_sequences=False)(attention_mul)
output = Dense(1, activation='sigmoid')(attention_mul)
model = Model(input=[inputs], output=output)
return model
if __name__ == '__main__':
N = 300000
# N = 300 -> too few = no training
inputs_1, outputs = get_data_recurrent(N, TIME_STEPS, INPUT_DIM)
if APPLY_ATTENTION_BEFORE_LSTM:
m = model_attention_applied_before_lstm()
else:
m = model_attention_applied_after_lstm()
m.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
print(m.summary())
m.fit([inputs_1], outputs, epochs=1, batch_size=64, validation_split=0.1)
attention_vectors = []
for i in range(300):
inputs = Input(shape=(
TIME_STEPS,
INPUT_DIM,
))
attention_mul = attention_3d_block(inputs)
attention_mul = LSTM(LSTM_UNITS, return_sequences=False)(attention_mul)
output = Dense(1, activation='sigmoid')(attention_mul)
model = Model(input=[inputs], output=output)
return model
if __name__ == '__main__':
N = 300000
#N = 300 #-> too few = no training
inputs_1, outputs, attention_columns = get_data_recurrent(
N, TIME_STEPS, INPUT_DIM, 5)
# for debug
for ntest in [0, 1]:
print(len(inputs_1[ntest]), inputs_1[ntest], inputs_1[ntest][10])
print(outputs[ntest])
print(attention_columns[ntest])
if APPLY_ATTENTION_BEFORE_LSTM:
m = model_attention_applied_before_lstm()
else:
m = model_attention_applied_after_lstm()
m.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
def model_attention_applied_before_lstm():
inputs = Input(shape=(TIME_STEPS, INPUT_DIM,))
attention_mul = attention_3d_block(inputs)
lstm_units = 32
attention_mul = LSTM(lstm_units, return_sequences=False)(attention_mul)
output = Dense(1, activation='sigmoid')(attention_mul)
model = Model(input=[inputs], output=output)
return model
if __name__ == '__main__':
N = 300000
# N = 300 -> too few = no training
inputs_1, outputs = get_data_recurrent(N, TIME_STEPS, INPUT_DIM)
if APPLY_ATTENTION_BEFORE_LSTM:
m = model_attention_applied_before_lstm()
else:
m = model_attention_applied_after_lstm()
m.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
print(m.summary())
m.fit([inputs_1], outputs, epochs=1, batch_size=64, validation_split=0.1)
attention_vectors = []
for i in range(300):
testing_inputs_1, testing_outputs = get_data_recurrent(1, TIME_STEPS, INPUT_DIM)
attention_vector = np.mean(get_activations(m,
TIME_STEPS,
INPUT_DIM,
))
attention_mul = attention_3d_block(inputs)
lstm_units = 32
attention_mul = LSTM(lstm_units, return_sequences=False)(attention_mul)
output = Dense(1, activation='sigmoid')(attention_mul)
model = Model(input=[inputs], output=output)
return model
if __name__ == '__main__':
N = 300000
# N = 300 -> too few = no training
inputs_1, inputs_2, outputs, primary_data, scaler = get_data_recurrent(
N, TIME_STEPS, INPUT_DIM, NN_FEATURES)
print(inputs_2.shape)
if not_use_LSTM:
m = model_ANN()
else:
m = model_attention_applied_after_lstm() # 目前正在用的
adam = Adam(lr=1.e-4)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')
m.compile(optimizer='adam', loss='mse', metrics=['mse']) # rmsprop
# m.compile(optimizer='sgd', loss=myloss, metrics=['mae'])
last_for_train = 500 # 500
print(m.summary())
# 16 - 8 : 0.8
# 12 - 8
