如何将张量转换为 numpy 数组 [英] How to convert tensor to numpy array

问题描述

我是 tensorflow 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?

I'm beginner of tensorflow. I made simple autoencoder with the help. I want to convert final decoded tensor to numpy array.I tried using .eval() but I could not work it. how can I convert tensor to numpy?

我的输入图片大小为 512*512*1，数据类型为原始图片格式.

My input image size is 512*512*1 and data type is raw image format.

#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

推荐答案

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

You can add decoded to the list of tensors to be returned by sess.run(), as follows. decoded_val will by numpy array, and you can reshape it to get the original image shape.

或者，您可以在训练循环之外执行 sess.run() 以获得结果解码图像.

Alternatively, you can do sess.run() outside of training loop to get the resulting decoded image.

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!')

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