我是TensorFlow的新手。我的数据集中的每个图像都有16x16的大小。 这是我的代码，我收到了这个错误。我提供了每个卷积层形状的代码和更多信息。
我很感激你的帮助。
def my_conv_net(input_data):
batch_size = 100
learning_rate = 0.005
evaluation_size = 500
# image_width = ttrainData[0].shape[0]
# image_height = ttrainData[0].shape[1]
image_width = 16
image_height = 16
# target_size = max(ttrainLabels) + 1
num_channels = 1
generations = 500
eval_every = 5
conv1_features = 32
conv2_features = 64
max_pool_size1 = 2
max_pool_size2 = 2
fully_connected_size1 = 100
dropout_prob = 0.75
# First Conv-ReLU-MaxPool Layer
conv1 = tf.nn.conv2d(input_data, conv1_weight, strides=[1, 1, 1, 1], padding='SAME')
relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_bias))
max_pool1 = tf.nn.max_pool(relu1, ksize=[1, max_pool_size1, max_pool_size1, 1], strides=[1, max_pool_size1, max_pool_size1, 1], padding='SAME')
print("input_data>>>: ", input_data)
print("conv1_weight>>>: ", conv1_weight)
print("conv1_weight get shape: ", conv1_weight.get_shape())
print("conv1_bias>>>: ", conv1_bias)
print("conv1>>>: ", conv1)
print("relu1>>>: ", relu1)
print("max_pool1>>>: ", max_pool1)
print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\n")
# Second Conv-ReLU-MaxPool Layer
conv2 = tf.nn.conv2d(max_pool1, conv2_weight, strides=[1, 1, 1, 1], padding='SAME')
relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_bias))
max_pool2 = tf.nn.max_pool(relu2, ksize=[1, max_pool_size2, max_pool_size2, 1], strides=[1, max_pool_size2, max_pool_size2, 1], padding='SAME')
print("conv2_weight>>>: ", conv2_weight)
print("conv2_bias>>>: ", conv2_bias)
print("conv2>>>: ", conv2)
print("relu2>>>: ", relu2)
print("max_pool2>>>: ", max_pool2)
print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\n")
# Transform Output into a 1xN layer for next fully connected layer
final_conv_shape = max_pool2.get_shape().as_list()
final_shape = final_conv_shape[1] * final_conv_shape[2] * final_conv_shape[3]
flat_output = tf.reshape(max_pool2, [final_conv_shape[0], final_shape])
# First Fully Connected Layer
print("flat_output ***: ", type(flat_output))
print("flat_output ***: ", flat_output)
print("full1_weight ***: ", type(full1_weight))
print("full1_weight ***: ", full1_weight)
print("full1_bias ***: ", type(full1_bias))
print("full1_bias ***: ", full1_bias)
fully_connected1 = tf.nn.relu(tf.add(tf.matmul(flat_output, full1_weight), full1_bias))
# Second Fully Connected Layer
final_model = tf.add(tf.matmul(fully_connected1, full2_weight), full2_bias)
# Add dropout
final_model_output = tf.nn.dropout(final_model, dropout)
return(final_model_output)
这就是我得到的。
ttrainData type: <class 'numpy.ndarray'>
ttrainData shape: (279, 16, 16)
train_data type: <class 'numpy.ndarray'>
teval_dataArray shape: (120, 16, 16)
ttrainData[0]: [[ 31. 30. 29. ..., 34. 41. 43.]
[ 31. 31. 30. ..., 33. 38. 40.]
[ 31. 32. 32. ..., 32. 37. 39.]
...,
[ 26. 30. 37. ..., 48. 52. 54.]
[ 32. 34. 39. ..., 45. 46. 45.]
[ 42. 41. 42. ..., 45. 41. 37.]]
ttrainData[0]: (16, 16)
ttestData[0]: [[ 116. 101. 86. ..., 96. 89. 82.]
[ 125. 111. 96. ..., 91. 86. 81.]
[ 130. 119. 106. ..., 88. 87. 85.]
...,
[ 120. 117. 116. ..., 107. 107. 111.]
[ 123. 120. 120. ..., 115. 114. 118.]
[ 126. 122. 122. ..., 121. 119. 123.]]
ttestData[0]: (16, 16)
$$$$$$$$$$$$$$$$$ checking1 $$$$$$$$$$$$$$$$$
image_width: 16
image_height: 16
Type of fully_connected_size1: <class 'int'>
fully_connected_size1 is: 100
Type of target_size: <class 'numpy.float64'>
target_size is: 2.0
*********convert float to int 1st way: 2
*********convert float to int: <class 'int'>
$$$$$$$$$$$$$$$$ checking2 $$$$$$$$$$$$$$$$$$$
checking3
checking4
checking5
input_data>>>: Tensor("Placeholder_31:0", shape=(100, 16, 16, 1), dtype=float32)
conv1_weight>>>: Tensor("Variable/read:0", shape=(4, 4, 1, 32), dtype=float32)
conv1_weight get shape: (4, 4, 1, 32)
conv1_bias>>>: Tensor("Variable_1/read:0", shape=(32,), dtype=float32)
conv1>>>: Tensor("Conv2D_14:0", shape=(100, 16, 16, 32), dtype=float32)
relu1>>>: Tensor("Relu_20:0", shape=(100, 16, 16, 32), dtype=float32)
max_pool1>>>: Tensor("MaxPool_14:0", shape=(100, 8, 8, 32), dtype=float32)
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
conv2_weight>>>: Tensor("Variable_2/read:0", shape=(4, 4, 32, 64), dtype=float32)
conv2_bias>>>: Tensor("Variable_3/read:0", shape=(64,), dtype=float32)
conv2>>>: Tensor("Conv2D_15:0", shape=(100, 8, 8, 64), dtype=float32)
relu2>>>: Tensor("Relu_21:0", shape=(100, 8, 8, 64), dtype=float32)
max_pool2>>>: Tensor("MaxPool_15:0", shape=(100, 4, 4, 64), dtype=float32)
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
flat_output ***: <class 'tensorflow.python.framework.ops.Tensor'>
flat_output ***: Tensor("Reshape_13:0", shape=(100, 1024), dtype=float32)
full1_weight ***: <class 'tensorflow.python.ops.variables.Variable'>
full1_weight ***: Tensor("Variable_4/read:0", shape=(3136, 100), dtype=float32)
full1_bias ***: <class 'tensorflow.python.ops.variables.Variable'>
full1_bias ***: Tensor("Variable_5/read:0", shape=(100,), dtype=float32)
Traceback (most recent call last):
File "<ipython-input-22-1db506e814de>", line 1, in <module>
ValueError: Dimensions must be equal, but are 1024 and 3136 for 'MatMul_15' (op: 'MatMul') with input shapes: [100,1024], [3136,100].
答案 0 :(得分：2)
你必须将shape =（100,16,16,1）更改为shape =（ - 1,16,16,1） ＆＃34;对于批量大小指示-1，它指定应根据输入中的输入值数量动态计算此维度＆＃34;