ConcatOp：输入的尺寸应匹配

Question

我正在使用张量流和python开发深度学习模型：

• 首先，使用CNN图层获取要素。
• 其次，重塑功能图，我想使用LSTM层。

但是，尺寸不匹配的错误...

ConcatOp：输入的尺寸应匹配：shape[0] = [71,48]vs.shape[1] = [1200,24]

W_conv1 = weight_variable([1,conv_size,1,12])
b_conv1 = bias_variable([12])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1)+ b_conv1)
h_pool1 = max_pool_1xn(h_conv1)

W_conv2 = weight_variable([1,conv_size,12,24])
b_conv2 = bias_variable([24])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_1xn(h_conv2)

W_conv3 = weight_variable([1,conv_size,24,48])
b_conv3 = bias_variable([48])

h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
h_pool3 = max_pool_1xn(h_conv3)


print(h_pool3.get_shape())
h3_rnn_input = tf.reshape(h_pool3, [-1,x_size/8,48])

num_layers = 1
lstm_size = 24
num_steps = 4

lstm_cell = tf.nn.rnn_cell.LSTMCell(lstm_size, initializer = tf.contrib.layers.xavier_initializer(uniform = False))
cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell]*num_layers)
init_state = cell.zero_state(batch_size,tf.float32)


cell_outputs = []
state = init_state
with tf.variable_scope("RNN") as scope:
for time_step in range(num_steps):
    if time_step > 0: scope.reuse_variables() 
    cell_output, state = cell(h3_rnn_input[:,time_step,:],state) ***** Error In here...

Answer 1

当您输入到rnn单元格时，输入张量和状态张量的批处理大小应相同。

在错误消息中，它说h3_rnn_input[:,time_step,:]形状为，[71,48]而state形状为[1200,24]

您需要做的是使第一个尺寸（batch_size）相同。

如果不希望使用数字71，请检查卷积部分。步幅/填充可能很重要。