如何将张量转换为 numpy 数组答案

【问题标题】：How to convert tensor to numpy array如何将张量转换为 numpy 数组
【发布时间】：2016-12-08 09:11:07
【问题描述】：

我是张量流的初学者。我在帮助下制作了简单的自动编码器。我想将最终的 decoded 张量转换为 numpy 数组。我尝试使用 .eval() 但我无法使用它。如何将张量转换为 numpy？

我的输入图片大小为512*512*1，数据类型为原始图片格式。

代码

#input
image_size = 512
hidden = 256
input_image = np.fromfile('PATH',np.float32)

# Variables
x_placeholder = tf.placeholder("float", (image_size*image_size))

x = tf.reshape(x_placeholder, [image_size * image_size, 1])
w_enc = tf.Variable(tf.random_normal([hidden, image_size * image_size], mean=0.0, stddev=0.05))
w_dec = tf.Variable(tf.random_normal([image_size * image_size, hidden], mean=0.0, stddev=0.05))
b_enc = tf.Variable(tf.zeros([hidden, 1]))
b_dec = tf.Variable(tf.zeros([image_size * image_size, 1]))

#model
encoded = tf.sigmoid(tf.matmul(w_enc, x) + b_enc)
decoded = tf.sigmoid(tf.matmul(w_dec,encoded) + b_dec)

# Cost Function
cross_entropy = -1. * x * tf.log(decoded) - (1. - x) * tf.log(1. - decoded)
loss = tf.reduce_mean(cross_entropy)
train_step = tf.train.AdagradOptimizer(0.1).minimize(loss)

# Train
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    print('Training...')
    for _ in xrange(10):
        loss_val, _ = sess.run([loss, train_step], feed_dict = {x_placeholder: input_image})
        print loss_val

【问题讨论】：

标签： python numpy tensorflow autoencoder

【解决方案1】：

您可以将decoded添加到sess.run()要返回的张量列表中，如下所示。 decoded_val 将通过 numpy 数组，您可以对其进行 reshape 以获得原始图像形状。

或者，您可以在训练循环之外执行 sess.run() 以获取生成的解码图像。

import tensorflow as tf
import numpy as np

tf.reset_default_graph()

#load_image
image_size = 16
k = 64
temp = np.zeros((image_size, image_size))


# Variables
x_placeholder = tf.placeholder("float", (image_size, image_size))

x = tf.reshape(x_placeholder, [image_size * image_size, 1])
w_enc = tf.Variable(tf.random_normal([k, image_size * image_size], mean=0.0, stddev=0.05))
w_dec = tf.Variable(tf.random_normal([image_size * image_size, k], mean=0.0, stddev=0.05))
b_enc = tf.Variable(tf.zeros([k, 1]))
b_dec = tf.Variable(tf.zeros([image_size * image_size, 1]))

#model
encoded = tf.sigmoid(tf.matmul(w_enc, x) + b_enc)
decoded = tf.sigmoid(tf.matmul(w_dec,encoded) + b_dec)


# Cost Function
cross_entropy = -1. * x * tf.log(decoded) - (1. - x) * tf.log(1. - decoded)
loss = tf.reduce_mean(cross_entropy)
train_step = tf.train.AdagradOptimizer(0.1).minimize(loss)

# Train
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    print('Training...')
    for _ in xrange(10):
      loss_val, decoded_val, _ = sess.run([loss, decoded, train_step], feed_dict = {x_placeholder: temp})
      print loss_val
    print('Done!')

【讨论】：