zhoujie2104231
2757a4d0d2
添加了Stock-Prediction-Models项目的多个文件，包括数据集、模型代码、README文档和CSS样式文件。这些文件用于股票预测模型的训练和展示，涵盖了LSTM、GRU等深度学习模型的应用。
41 lines
1.9 KiB
Python
41 lines
1.9 KiB
Python
import tensorflow as tf
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import numpy as np
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import time
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def reducedimension(input_, dimension = 2, learning_rate = 0.01, hidden_layer = 256, epoch = 20):
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input_size = input_.shape[1]
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X = tf.placeholder("float", [None, input_size])
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weights = {
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'encoder_h1': tf.Variable(tf.random_normal([input_size, hidden_layer])),
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'encoder_h2': tf.Variable(tf.random_normal([hidden_layer, dimension])),
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'decoder_h1': tf.Variable(tf.random_normal([dimension, hidden_layer])),
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'decoder_h2': tf.Variable(tf.random_normal([hidden_layer, input_size])),
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}
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biases = {
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'encoder_b1': tf.Variable(tf.random_normal([hidden_layer])),
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'encoder_b2': tf.Variable(tf.random_normal([dimension])),
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'decoder_b1': tf.Variable(tf.random_normal([hidden_layer])),
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'decoder_b2': tf.Variable(tf.random_normal([input_size])),
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}
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first_layer_encoder = tf.nn.sigmoid(tf.add(tf.matmul(X, weights['encoder_h1']), biases['encoder_b1']))
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second_layer_encoder = tf.nn.sigmoid(tf.add(tf.matmul(first_layer_encoder, weights['encoder_h2']), biases['encoder_b2']))
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first_layer_decoder = tf.nn.sigmoid(tf.add(tf.matmul(second_layer_encoder, weights['decoder_h1']), biases['decoder_b1']))
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second_layer_decoder = tf.nn.sigmoid(tf.add(tf.matmul(first_layer_decoder, weights['decoder_h2']), biases['decoder_b2']))
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cost = tf.reduce_mean(tf.pow(X - second_layer_decoder, 2))
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optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)
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sess = tf.InteractiveSession()
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sess.run(tf.global_variables_initializer())
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for i in range(epoch):
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last_time = time.time()
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_, loss = sess.run([optimizer, cost], feed_dict={X: input_})
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if (i + 1) % 10 == 0:
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print('epoch:', i + 1, 'loss:', loss, 'time:', time.time() - last_time)
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vectors = sess.run(second_layer_encoder, feed_dict={X: input_})
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tf.reset_default_graph()
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return vectors |