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tensorflow 笔记2 双向LSTM

谎言梦发表于:8 月 11 日 09:09回复(1)

tensorflow 笔记2 双向LSTM
数据文件颇大 有空再传

BasicLSTM¶

%%time
from __future__ import division
from __future__ import print_function  
import numpy as np
import pandas as pd
import matplotlib.pylab as plt
%matplotlib inline
import seaborn as sns
import tensorflow as tf
from tensorflow.python.ops import rnn, rnn_cell
fac = np.load('/home/big/Quotes/TensorFlow deal with Uqer/fac16.npy').astype(np.float32)
ret = np.load('/home/big/Quotes/TensorFlow deal with Uqer/ret16.npy').astype(np.float32)
# 数据格式 日期-多因子 例如 (09-01 Ab1 Ab2 Ab3 )(09-02 Ab1 Ab2 Ab3) 
# Parameters
learning_rate = 0.001
batch_size = 1024
training_iters = int(fac.shape[0]/batch_size)
display_step = 10

# Network Parameters
n_input = 17
n_steps = 40
n_hidden = 1024
n_classes = 7

# tf Graph input
x = tf.placeholder('float',[None, n_steps, n_input])
y = tf.placeholder('float',[None, n_classes])

# Define weights
weights = {
    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))
}
biases = {
    'out': tf.Variable(tf.random_normal([n_classes]))
}

def BasicLSTM(x, weights, biases):
    x = tf.transpose(x, [1, 0, 2])
    x = tf.reshape(x, [-1,n_input])
    x = tf.split(0, n_steps, x)
    # 这一段不用注意,因为使用CNN提取的npy数据这里进行数据处理,转换成格式为
    # 日期-一批次数据(多只股票)-多因子数据,相当于将多只股票的多因子数据以时间序列一天一天喂给RNN模型
    
    Basicl_LSTM_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)
    outputs, states = tf.nn.rnn(Basicl_LSTM_cell, x, dtype=tf.float32)
    return tf.matmul(outputs[-1], weights['out']) + biases['out']

pred = BasicLSTM(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()   
# Launch the graph
with tf.Session() as sess:
    sess.run(init)
    step = 1
    for step in range(1):
        for i in range(int(len(fac)/batch_size)):
            batch_x = fac[i*batch_size:(i+1)*batch_size].reshape([batch_size,n_steps,n_input])
            batch_y = ret[i*batch_size:(i+1)*batch_size].reshape([batch_size,n_classes])
            sess.run(optimizer,feed_dict={x:batch_x,y:batch_y})           
            if i % display_step ==0:
                print(i,'----',(int(len(fac)/batch_size)))
        loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,y: batch_y})
        print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
                  "{:.6f}".format(loss) + ", Training Accuracy= " + \
                  "{:.5f}".format(acc))
    print("Optimization Finished!")   
    # Calculate accuracy for 128 mnist test images
    test_len = 1280
    test_data = fac[:test_len].reshape([batch_size,n_steps,n_input])
    test_label = ret[:test_len].reshape([batch_size,n_classes])

    print("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={x: test_data, y: test_label}))
    
    sess.close()    

双向LSTM¶

%%time
from __future__ import division
from __future__ import print_function  
import numpy as np
import pandas as pd
import matplotlib.pylab as plt
%matplotlib inline
import seaborn as sns

import tensorflow as tf

fac = np.load('/home/big/Quotes/TensorFlow deal with Uqer/fac16.npy').astype(np.float32)
ret = np.load('/home/big/Quotes/TensorFlow deal with Uqer/ret16.npy').astype(np.float32)
# 数据格式 日期-多因子 例如 (09-01 Ab1 Ab2 Ab3 )(09-02 Ab1 Ab2 Ab3) 

# Parameters
learning_rate = 0.001
training_iters = 100000
batch_size = 1280
display_step = 10

# Network Parameters
n_input = 17 # MNIST data input (img shape: 28*28)
n_steps = 40 # timesteps
n_hidden = 128 # hidden layer num of features
n_classes = 7 # MNIST total classes (0-9 digits)


# tf Graph input
x = tf.placeholder("float", [None, n_steps, n_input])
y = tf.placeholder("float", [None, n_classes])

# Define weights
weights = {
    # Hidden layer weights => 2*n_hidden because of forward + backward cells
    'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes]))
}
biases = {
    'out': tf.Variable(tf.random_normal([n_classes]))
}

def BiRNN(x, weights, biases):

    # Prepare data shape to match `bidirectional_rnn` function requirements
    # Current data input shape: (batch_size, n_steps, n_input)
    # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)

    # Permuting batch_size and n_steps
    x = tf.transpose(x, [1, 0, 2])
    # Reshape to (n_steps*batch_size, n_input)
    x = tf.reshape(x, [-1, n_input])
    # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)
    x = tf.split(0, n_steps, x)

    # Define lstm cells with tensorflow
    # Forward direction cell
    lstm_fw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)
    # Backward direction cell
    lstm_bw_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)

    # Get lstm cell output
    try:
        outputs, _, _ = tf.nn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,
                                              dtype=tf.float32)
    except Exception: # Old TensorFlow version only returns outputs not states
        outputs = tf.nn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,
                                        dtype=tf.float32)

    # Linear activation, using rnn inner loop last output
    return tf.matmul(outputs[-1], weights['out']) + biases['out']
pred = BiRNN(x, weights, biases)

# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Evaluate model
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)
    step = 1
    for step in range(100):
        for i in range(int(len(fac)/batch_size)):
            batch_x = fac[i*batch_size:(i+1)*batch_size].reshape([batch_size,n_steps,n_input])
            batch_y = ret[i*batch_size:(i+1)*batch_size].reshape([batch_size,n_classes])
            sess.run(optimizer,feed_dict={x:batch_x,y:batch_y})           
            if i % display_step ==0:
                print(i,'----',(int(len(fac)/batch_size)))
        loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,y: batch_y})
        print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \
                  "{:.6f}".format(loss) + ", Training Accuracy= " + \
                  "{:.5f}".format(acc))
    print("Optimization Finished!")   
    # Calculate accuracy for 128 mnist test images
    test_len = 1280
    test_data = fac[:test_len].reshape([batch_size,n_steps,n_input])
    test_label = ret[:test_len].reshape([batch_size,n_classes])

    print("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={x: test_data, y: test_label}))
    
    sess.close()    
 

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