Attention+GRU
数据集
纳斯达克100
模型原理
模型代码
class Attention(Layer):
def __init__(self, step_dim,
W_regularizer=None, b_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, **kwargs):
self.supports_masking = True
self.init = tf.keras.initializers.get('glorot_uniform')
# W_regularizer: 权重上的正则化
# b_regularizer: 偏置项的正则化
self.W_regularizer = tf.keras.regularizers.get(W_regularizer)
self.b_regularizer = tf.keras.regularizers.get(b_regularizer)
# W_constraint: 权重上的约束项
# b_constraint: 偏置上的约束项
self.W_constraint = tf.keras.constraints.get(W_constraint)
self.b_constraint = tf.keras.constraints.get(b_constraint)
self.bias = bias
self.step_dim = step_dim
self.features_dim = 0
super(Attention, self).__init__(**kwargs)
def build(self, input_shape):
assert len(input_shape) == 3
self.W = self.add_weight(shape=(input_shape[-1],),
initializer=self.init,
name='{}_W'.format(self.name),
regularizer=self.W_regularizer,
constraint=self.W_constraint)
self.features_dim = input_shape[-1]
if self.bias:
self.b = self.add_weight(shape=(input_shape[1],),
initializer='zero',
name='{}_b'.format(self.name),
regularizer=self.b_regularizer,
constraint=self.b_constraint)
else:
self.b = None
self.built = True
def compute_mask(self, input, input_mask=None):
return None
def get_config(self):
config = super().get_config().copy()
config.update({
'step_dim': self.step_dim,
'W_regularizer': self.W_regularizer,
'b_regularizer': self.b_regularizer,
'W_constraint': self.W_constraint,
'b_constraint': self.b_constraint,
'bias': self.bias,
})
return config
def call(self, x, mask=None):
features_dim = self.features_dim
step_dim = self.step_dim
eij = K.reshape(K.dot(K.reshape(x, (-1, features_dim)),
K.reshape(self.W, (features_dim, 1))), (-1, step_dim))
if self.bias:
eij += self.b
eij = K.tanh(eij)
a = K.exp(eij)
'''
keras.backend.cast(x, dtype): 将张量转换到不同的 dtype 并返回
'''
if mask is not None:
a *= K.cast(mask, K.floatx())
'''
keras.backend.epsilon(): 返回浮点数
'''
a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())
a = K.expand_dims(a)
weighted_input = x * a
return K.sum(weighted_input, axis=1)
def compute_output_shape(self, input_shape):
return input_shape[0], self.features_dim
完整代码
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow.keras.backend as K
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import *
from tensorflow.keras.initializers import *
from tensorflow.keras.layers import *
from tensorflow.keras.models import *
from tensorflow.python.keras.layers import Layer
file_path = "/Users/Arithmetic/pythonProject/lstm+attention/nasdaq100_padding.csv"
df = pd.read_csv(file_path)
plt.figure()
plt.plot(range(df.shape[0]), (df['NDX']))
plt.xlabel('time', fontsize=18)
plt.ylabel('NDX', fontsize=18)
plt.show()
BATCH_SIZE = 128
EPOCHS = 3
SEQ_LEN = 10
FUTURE_PERIOD_PREDICT = 1
RATIO_TO_PREDICT = "NDX"
targ_cols = ("NDX",)
# 归一化，划分feature target
def preprocess_data(dat, col_names):
scale = MinMaxScaler().fit(dat)
proc_dat = scale.transform(dat)
mask = np.ones(proc_dat.shape[1], dtype=bool)
dat_cols = list(dat.columns)
for col_name in col_names:
mask[dat_cols.index(col_name)] = False
feats = proc_dat[:, mask]
targs = proc_dat[:, ~mask]
return feats, targs
# 把数据切分成80%训练集、20%测试集
def split_data(data, percent_train=0.70):
num_rows = len(data)
train_data, test_data = [], []
for idx, row in enumerate(data):
if idx < num_rows * percent_train:
train_data.append(row)
else:
test_data.append(row)
return np.array(train_data), np.array(test_data)
data_X, data_y = preprocess_data(df, targ_cols)
print(data_X.shape,data_y.shape)
all_data = np.concatenate([data_X, data_y], axis=1)
print(all_data.shape)
train_, test_ = split_data(all_data)
print("train shape {0}".format(train_.shape))
print("test shape {0}".format(test_.shape))
def timestamp_data(data):
X = []
Y = []
for i in range(SEQ_LEN, len(data)-FUTURE_PERIOD_PREDICT+1):
X.append(data[i-SEQ_LEN:i, :data_X.shape[1]]) # 含左不含右
Y.append(data[i+(FUTURE_PERIOD_PREDICT-1), data_X.shape[1]])
return np.array(X), np.array(Y)
X_train, y_train = timestamp_data(train_)
print('X_train', X_train.shape,'y_train', y_train.shape)
X_test, y_test = timestamp_data(test_)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], data_X.shape[1]))
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], data_X.shape[1]))
print("train shape {0}".format(X_train.shape))
# print("valid shape {0}".format(X_valid.shape))
print("test shape {0}".format(X_test.shape))
class Attention(Layer):
def __init__(self, step_dim,
W_regularizer=None, b_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, **kwargs):
self.supports_masking = True
self.init = tf.keras.initializers.get('glorot_uniform')
# W_regularizer: 权重上的正则化
# b_regularizer: 偏置项的正则化
self.W_regularizer = tf.keras.regularizers.get(W_regularizer)
self.b_regularizer = tf.keras.regularizers.get(b_regularizer)
# W_constraint: 权重上的约束项
# b_constraint: 偏置上的约束项
self.W_constraint = tf.keras.constraints.get(W_constraint)
self.b_constraint = tf.keras.constraints.get(b_constraint)
self.bias = bias
self.step_dim = step_dim
self.features_dim = 0
super(Attention, self).__init__(**kwargs)
def build(self, input_shape):
assert len(input_shape) == 3
self.W = self.add_weight(shape=(input_shape[-1],),
initializer=self.init,
name='{}_W'.format(self.name),
regularizer=self.W_regularizer,
constraint=self.W_constraint)
self.features_dim = input_shape[-1]
if self.bias:
self.b = self.add_weight(shape=(input_shape[1],),
initializer='zero',
name='{}_b'.format(self.name),
regularizer=self.b_regularizer,
constraint=self.b_constraint)
else:
self.b = None
self.built = True
def compute_mask(self, input, input_mask=None):
return None
def call(self, x, mask=None):
features_dim = self.features_dim
step_dim = self.step_dim
eij = K.reshape(K.dot(K.reshape(x, (-1, features_dim)),
K.reshape(self.W, (features_dim, 1))), (-1, step_dim))
if self.bias:
eij += self.b
eij = K.tanh(eij)
a = K.exp(eij)
'''
keras.backend.cast(x, dtype): 将张量转换到不同的 dtype 并返回
'''
if mask is not None:
a *= K.cast(mask, K.floatx())
'''
keras.backend.epsilon(): 返回浮点数
'''
a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon() , K.floatx())
a = K.expand_dims(a)
weighted_input = x * a
return K.sum(weighted_input, axis=1)
def compute_output_shape(self, input_shape):
return input_shape[0], self.features_dim
inp = Input(shape=(SEQ_LEN, data_X.shape[1]))
x = GRU(256, return_sequences=True)(inp)
x = Dropout(0.2)(x)
x = BatchNormalization()(x)
x = Attention(SEQ_LEN)(x)
x = Dense(32, activation="relu")(x)
x = Dropout(0.2)(x)
x = Dense(1, activation="relu")(x)
model_lstm_attention = tf.keras.Model(inputs=inp, outputs=x)
model_lstm_attention.compile(loss='mean_squared_error', optimizer='adam')
model_lstm_attention.summary()
earlyStopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, verbose=1, mode='auto')
tf.keras.utils.plot_model(model_lstm_attention,
to_file="model_lstm_attention.png",
show_shapes=True)
model_lstm_attention.fit(X_train, y_train,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=(X_test, y_test))
# model_lstm_attention.save('lstm+gru.h5')
predicted_LSTM_Att = model_lstm_attention.predict(X_test)
#绘图
plt.plot(list(range(X_test.shape[0])), y_test, label='True')
plt.plot(list(predicted_LSTM_Att), label='Predicted Test')
plt.title('ffmpeg-power')
plt.xlabel('DateTime')
plt.ylabel('power')
plt.legend()
plt.savefig("/Users/Arithmetic/pythonProject/lstm+attention/result_pics/lstm+attention_nasdaq_epoch{0}.png".format(EPOCHS))
plt.show()
结果图