我想制作一个ConvNet，其输出大小与输入之一相同。因此，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，所以我只将一个卷积层设置为零填充，以将输出大小设为与输入相同。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，但似乎无法完成这项工作。
问题是您的convnet输出的形状为（None，64），但是您提供的目标数据（标签）的形状为（None，400，400）。我不确定要对代码做什么，是否要进行一些自动编码？还是用于分类任务？
对于自动编码器，以下是针对MNIST的卷积自动编码器，您可以将其与您自己的数据相适应并更改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()
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我来说两句