python之ConvLSTMCell : Dimensions of inputs should match 的 Tensorflow 错误

我尝试根据 Tensorflow 文档输入 ConvLSTMCell 输入参数，但我仍然收到此错误:

InvalidArgumentError: ConcatOp : Dimensions of inputs should match: shape[0] = [10,64,64,1] vs. shape[1] = [1,64,64,16] 
     [[Node: rnn/while/rnn/Encoder_1/concat = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](rnn/while/TensorArrayReadV3, rnn/while/Switch_4:1, rnn/while/rnn/Encoder_1/split/split_dim)]] 

我的代码是:

num_channels = 1 
img_size = 64 
filter_size1 = 5  
num_filters1 = 16 
#If time_major == True, this must be a Tensor of shape: [max_time, batch_size, ...], or a nested tuple of such elements. 
x = tf.placeholder(tf.float32, shape=[None,1, img_size, img_size, num_channels], name='x') 
InputShape = [img_size,img_size, 1] 
encoder_1_KernelShape = [filter_size1,filter_size1] 
# create a ConvLSTMCell 
rnn_cell = ConvLSTMCell(2, InputShape, num_filters1, encoder_1_KernelShape, use_bias=True, forget_bias=1.0, name='Encoder_1') 
 
# 'outputs' is a tensor of shape [batch_size, max_time, cell_state_size] 
 
# defining initial state 
#initial_state = rnn_cell.zero_state(batch_size, dtype=tf.float32) 
initial_state = rnn_cell.zero_state(1, dtype=tf.float32) 
# 'state' is a tensor of shape [batch_size, cell_state_size] 
encoder_1_outputs, encoder_1_state = tf.nn.dynamic_rnn(rnn_cell, x, 
                                   initial_state=initial_state, 
                                   dtype=tf.float32) 
 
for i in range(2): 
    x_train = data_3[0:10, i, :, :] 
    x_train = x_train.flatten() 
    x_train = x_train.reshape([10, 1, img_size, img_size, 1]) 
    x_train = np.float32(x_train) 
    feed_dict_train = {x: x_train} 

请您参考如下方法:

试试这个:

num_channels = 1 
img_size = 64 
filter_size1 = 5 
num_filters1 = 16 
 
x = tf.placeholder(tf.float32, shape=[None,None,img_size,img_size,num_channels], 
                   name='x') 
InputShape = [img_size, img_size, num_channels] 
encoder_1_KernelShape = [filter_size1, filter_size1] 
rnn_cell = ConvLSTMCell(2, InputShape, num_filters1, encoder_1_KernelShape, 
                        use_bias=True, forget_bias=1.0, name='Encoder_1') 
 
initial_state = rnn_cell.zero_state(10, dtype=tf.float32) 
encoder_1_outputs, encoder_1_state = tf.nn.dynamic_rnn(rnn_cell, x, 
                                                       initial_state=initial_state, 
                                                       dtype=tf.float32) 
 
with tf.Session() as sess: 
  sess.run(tf.global_variables_initializer()) 
  x_train = np.zeros([10, 1, img_size, img_size, num_channels], dtype=np.float32) 
  sess.run(encoder_1_outputs, feed_dict={x: x_train}) 

请注意，x 中的第一个维度是 batch_size(示例中等于 10)，第二个维度是 sequence_num(等于 1 ).