tf.reshape 在添加额外维度的情况下不起作用

Question

根据 tensorflow 网站，tf.reshape 将某个形状的张量映射到另一个形状的张量。我想将大小为 [600, 64] 的张量映射到大小为 [-1, 8, 8, 1] 的张量（其中 -1 位置的维度为 600）。但这似乎不起作用。

我在 python 3.6 的 tensorflow 上运行它，虽然它会重塑为 [-1, 8, 8] 之类的东西，但它不会重塑为 [-1, 8, 8, 1]

import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.preprocessing import LabelBinarizer

# preprocessing method needed
    def flatten(array):
        temp = []
        for j in array:
            temp.extend(j)
        return temp

# preprocess the data
digits = datasets.load_digits()
images = digits.images
images = [flatten(i) for i in images]
labels = digits.target
labels = LabelBinarizer().fit_transform(labels)

# the stats needed
width = 8
height = 8
alpha = 0.1
num_labels = 10
kernel_length = 3
batch_size = 10
channels = 1

# the tensorflow placeholders and reshaping
X = tf.placeholder(tf.float32, shape = [None, width * height * channels])

# AND NOW HERE IS WHERE THE ERROR STARTS
y_true = tf.placeholder(tf.float32, shape = [None, num_labels])
X = tf.reshape(X, [-1, 8, 8, 1])

# the convolutional model
conv1 = tf.layers.conv2d(X, filters = 32, kernel_size = [kernel_length,  kernel_length])
conv2 = tf.layers.conv2d(conv1, filters = 64, kernel_size = [2, 2])
flatten = tf.reshape(X, [-1, 1])
dense1 = tf.layers.dense(flatten, units=50, activation = tf.nn.relu)
y_pred = tf.layers.dense(dense1, units=num_labels, activation = tf.nn.softmax)

# the loss and training functions
loss = tf.losses.mean_squared_error(labels=y_true, predictions=y_pred)
train = tf.train.GradientDescentOptimizer(alpha).minimize(loss)

# initializing the variables and the tf.session
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

# running the session
for i in range(batch_size):
    _, lossVal = sess.run((train, loss), feed_dict = {X:images[:600], y_true: labels[:600]})
    print(lossVal)

我不断收到此错误： ValueError：无法为张量“重塑：0”提供形状（600、64）的值，其形状为“（？、8、8、1）” 而且我觉得不应该是这样，因为 8 * 8 * 1 确实等于 64。

Answer 1

images[:600] 的形状为(600, 64)，与占位符预期形状不对应，(None, 8, 8, 1)。

重塑您的数据或更改占位符的形状。

请注意，您最初将占位符形状定义为 (None, 64) 的事实是无关紧要的，因为您稍后会对其进行重新调整。