python之TensorFlow/TFLearn : ValueError: Cannot feed value of shape (256, 400, 400) 对于张量 u'TargetsData/Y : 0', which has shape ' ( , 64)'

我想制作一个输出大小与输入大小相同的 ConvNet。所以，我使用 TFLearn 库实现了它。因为我只是想要一个满足这些目的的简单示例，所以我只设置了一个带有零填充的卷积层，以获得与输入相同的输出大小。以下是代码:

X = X.reshape([-1, 400, 400, 1]) 
Y = Y.reshape([-1, 400, 400, 1]) 
testX = testX.reshape([-1, 400, 400, 1]) 
testY = testY.reshape([-1, 400, 400, 1]) 
X, mean = du.featurewise_zero_center(X) 
testX = du.featurewise_zero_center(testX, mean) 
 
 
# Building a Network 
net = tflearn.input_data(shape=[None, 400, 400, 1]) 
net = tflearn.conv_2d(net, 64, 3, padding='same', activation='relu', bias=False) 
sgd = tflearn.SGD(learning_rate=0.1, lr_decay=0.96, decay_step=300) 
net = tflearn.regression(net, optimizer='sgd', 
                     loss='categorical_crossentropy', 
                     learning_rate=0.1) 
# Training 
model = tflearn.DNN(net, checkpoint_path='model_network', 
                max_checkpoints=10, tensorboard_verbose=3) 
model.fit(X, Y, n_epoch=100, validation_set=(testX, testY), 
      show_metric=True, batch_size=256, run_id='network_test') 

但是，这些代码会产生错误

ValueError: Cannot feed value of shape (256, 400, 400) for Tensor u'TargetsData/Y:0', which has shape '(?, 64)' 

我已经搜索并检查了一些文档，但我似乎无法完成这项工作。

请您参考如下方法:

问题是您的卷积网络输出的形状为 (None, 64)，但您给出的目标数据(标签)的形状为 (None, 400, 400)。我不确定你想用你的代码做什么，你是想做一些自动编码吗？还是用于分类任务？

对于自动编码器，以下是 MNIST 的卷积自动编码器，您可以使用自己的数据调整它并更改 input_data 形状:

from __future__ import division, print_function, absolute_import 
 
import numpy as np 
import matplotlib.pyplot as plt 
import tflearn 
import tflearn.data_utils as du 
 
# Data loading and preprocessing 
import tflearn.datasets.mnist as mnist 
X, Y, testX, testY = mnist.load_data(one_hot=True) 
 
X = X.reshape([-1, 28, 28, 1]) 
testX = testX.reshape([-1, 28, 28, 1]) 
X, mean = du.featurewise_zero_center(X) 
testX = du.featurewise_zero_center(testX, mean) 
 
# Building the encoder 
encoder = tflearn.input_data(shape=[None, 28, 28, 1]) 
encoder = tflearn.conv_2d(encoder, 16, 3, activation='relu') 
encoder = tflearn.max_pool_2d(encoder, 2) 
encoder = tflearn.conv_2d(encoder, 8, 3, activation='relu') 
decoder = tflearn.upsample_2d(encoder, 2) 
decoder = tflearn.conv_2d(encoder, 1, 3, activation='relu') 
 
# Regression, with mean square error 
net = tflearn.regression(decoder, optimizer='adam', learning_rate=0.001, 
                         loss='mean_square', metric=None) 
 
# Training the auto encoder 
model = tflearn.DNN(net, tensorboard_verbose=0) 
model.fit(X, X, n_epoch=10, validation_set=(testX, testX), 
          run_id="auto_encoder", batch_size=256) 
 
# Encoding X[0] for test 
print("\nTest encoding of X[0]:") 
# New model, re-using the same session, for weights sharing 
encoding_model = tflearn.DNN(encoder, session=model.session) 
print(encoding_model.predict([X[0]])) 
 
# Testing the image reconstruction on new data (test set) 
print("\nVisualizing results after being encoded and decoded:") 
testX = tflearn.data_utils.shuffle(testX)[0] 
# Applying encode and decode over test set 
encode_decode = model.predict(testX) 
# Compare original images with their reconstructions 
f, a = plt.subplots(2, 10, figsize=(10, 2)) 
for i in range(10): 
    a[0][i].imshow(np.reshape(testX[i], (28, 28))) 
    a[1][i].imshow(np.reshape(encode_decode[i], (28, 28))) 
f.show() 
plt.draw() 
plt.waitforbuttonpress()