def inference(args, cnn_features):
tf.reset_default_graph()
with tf.Session() as sess:
net = LSTM(sess, max_seq_len=25, h_dim=1024)
net.build_model()
net.inference(cnn_features=cnn_features,
label_file=args.label_file,
gen_from=args.gen_from,
out_path=args.output_folder,
bsize=args.bsize)
def main():
np.random.seed(RANDOM_SEED) # 方便结果重现
data = pd.read_csv(DATA_PATH)
scaler, data = preprocessing_data.process_normalization(
data=data,
useful_colmns=[
'Open', 'High', 'Low', 'Close', 'Volume', 'Market Cap', 'Weekday',
'Increase'
])
training_set, test_set = generate_batch.split_data(data)
training_inputs, train_outputs = generate_batch.generate_batchs(
training_set)
test_inputs, test_outputs = generate_batch.generate_batchs(test_set)
model = LSTM.build_model(inputs=training_inputs,
output_size=1,
LSTM_units=20)
history = model.fit(training_inputs,
train_outputs,
batch_size=1,
epochs=30,
verbose=2,
shuffle=True)
fig, ax1 = plt.subplots(1, 1)
ax1.plot(history.epoch, history.history['loss'])
ax1.set_title('Training loss')
ax1.set_ylabel('Mean Absolute Error (MAE)', fontsize=12)
ax1.set_xlabel('# Epochs', fontsize=12)
plt.show()
h = model.predict(test_inputs)
dat = np.concatenate((test_outputs, h), axis=1)
dat = dat * scaler.data_range_[-1] + scaler.data_min_[-1]
df = pd.DataFrame(dat, columns=['actual', 'prediction'])
df.to_csv('prediction.csv', index=False)
data = HistoryData(DATA_PATH)
data.date2weekday().add_increase_col().drop_columns(['Date']).scalar(
['Open*', 'High', 'Close**'], 0.01).split(0.8)
train_x, train_y, test_x, test_y = data.generate_train_test_data(
x_columns=['Open*', 'High', 'Close**', 'Weekday'],
y_columns=['Close**'],
window_len=7,
flatten=False)
print(train_x.shape)
print(test_x.shape)
print(train_y.shape)
print(test_y.shape)
# 模型训练
model = LSTM.build_model(inputs=train_x,
output_size=7,
LSTM_units=20,
loss='mean_squared_error')
history = model.fit(train_x,
train_y,
batch_size=32,
epochs=200,
verbose=1,
shuffle=True)
# loss可视化
fig, ax1 = plt.subplots(1, 1)
ax1.plot(history.epoch, history.history['loss'])
ax1.set_title('Training loss')
ax1.set_ylabel('Mean Absolute Error (MAE)', fontsize=12)
ax1.set_xlabel('# Epochs', fontsize=12)
plt.show()