title: TensorFlow: Incompatible shapes: [100,155] vs. [128,155] when combining CNN and LSTM
body: <h1>Problem</h1>
  
  <p>I'm getting an incompatible shape error when trying to stack a Conv -> Lstm -> Fully connected layers for an audio regression task. I can't work out why I'm getting the error I'm getting - the graph builds fine then throws the error - can anyone help?</p>
  
  <h1>Code</h1>
  
  <pre><code>lstm_num_hidden = 128
  lstm_number_layers = 3
  x = tf.placeholder(tf.float32, [None, 1024])
  y = tf.placeholder(tf.float32, [None, 155])
  keep_probability = tf.placeholder(tf.float32)
  
  def conv2d(x, weights):
      return tf.nn.conv2d(x, weights, strides=[1, 1, 1, 1], padding='SAME')
  
  x_spectrogram = tf.reshape(x, [-1, 32, 32, 1])
  
  conv1_weights = tf.Variable(tf.truncated_normal([5, 5, 1, 32], stddev=0.1))
  conv1_bias = tf.Variable(tf.constant(0.1, shape=[32]))
  conv1_hidden = tf.nn.relu(conv2d(x_spectrogram, conv1_weights) + conv1_bias)
  conv1_pooling = tf.nn.max_pool(conv1_hidden, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')
  
  conv2_weights = tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.1))
  conv2_bias = tf.Variable(tf.constant(0.1, shape=[64]))
  conv2_hidden = tf.nn.relu(conv2d(conv1_pooling, conv2_weights) + conv2_bias)
  conv2_pooling = tf.nn.max_pool(conv2_hidden, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')
  conv2_output = tf.reshape(conv2_pooling, [-1, 64, 64])
  
  # changes to [8, BatchSize, 8, 64]
  tr_x = tf.transpose(conv2_output, [1, 0, 2])
  re_x = tf.reshape(tr_x, [-1, 64])
  sp_x = tf.split(0, 64, re_x)
  
  lstm_cell = rnn_cell.LSTMCell(lstm_num_hidden, forget_bias=1.0, state_is_tuple=True)
  lstm_cell = rnn_cell.MultiRNNCell([lstm_cell] * lstm_number_layers, state_is_tuple=True)
  init_state = lstm_cell.zero_state(128, tf.float32)
  lstm_output, _ = rnn.rnn(cell=lstm_cell, inputs=sp_x, dtype=tf.float32, initial_state=init_state)
  lstm_weights = tf.Variable(tf.truncated_normal([lstm_num_hidden, 155], stddev=0.1))
  lstm_bias = tf.Variable(tf.truncated_normal([155], stddev=0.1))
  out = tf.add(tf.matmul(lstm_output[-1], lstm_weights), lstm_bias)
  
  fully_connected1_weights = tf.Variable(tf.truncated_normal([155, 1024], stddev=0.1))
  fully_connected1_biases = tf.Variable(tf.truncated_normal([1024], stddev=0.1))
  fully_connected1 = tf.nn.relu(tf.matmul(out, fully_connected1_weights) + fully_connected1_biases)
  fully_connected1_dropout = tf.nn.dropout(fully_connected1, keep_probability)
  
  fully_connected2_weights = tf.Variable(tf.truncated_normal([1024, 155], stddev=0.1))
  fully_connected2_biases = tf.Variable(tf.truncated_normal([155], stddev=0.1))
  prediction = tf.matmul(fully_connected1_dropout, fully_connected2_weights) + fully_connected2_biases
  
  def error(labels, prediction):
      return tf.sqrt(tf.reduce_mean(tf.square(tf.sub(labels, prediction))))
  
  rmse = error(y, prediction)
  optimise = tf.train.AdamOptimizer(1e-4).minimize(rmse)
  
  # Get training and testing batch.
  train_batch_x = np.load("train_x.npy")
  train_batch_y = np.load("train_y.npy")
  test_batch_x = np.load("test_x.npy")
  test_batch_y = np.load("test_y.npy")
  
  sess = tf.Session()
  sess.run(tf.global_variables_initializer())
  
  for i in range(100):
  
      for batch in trange(99, desc="Training"):
          start = batch * 100
          end = batch * 100 + 100
          sess.run(optimise, { x: train_batch_x[start:end],
                               y: train_batch_y[start:end],
                               keep_probability: 0.5 })
  
      rmse_error = sess.run(rmse, { x: test_batch_x,
                                    y: test_batch_y,
                                    keep_probability: 1.0 })
      print "Root Mean Squared Error:" + str(rmse_error)
  </code></pre>
  
  <h1>Error</h1>
  
  <pre><code>W tensorflow/core/framework/op_kernel.cc:975] Invalid argument: Incompatible shapes: [100,155] vs. [128,155]
       [[Node: gradients/Sub_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/Sub_grad/Shape, gradients/Sub_grad/Shape_1)]]
  W tensorflow/core/framework/op_kernel.cc:975] Invalid argument: Incompatible shapes: [100,155] vs. [128,155]
       [[Node: gradients/Sub_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/Sub_grad/Shape, gradients/Sub_grad/Shape_1)]]
  
  Traceback (most recent call last):
    File "conv_rnn_experiment.py", line 81, in &lt;module&gt;
      keep_probability: 0.5 })
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 766, in run
      run_metadata_ptr)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 964, in _run
      feed_dict_string, options, run_metadata)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1014, in _do_run
      target_list, options, run_metadata)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1034, in _do_call
      raise type(e)(node_def, op, message)
  tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [100,155] vs. [128,155]
       [[Node: gradients/Sub_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/Sub_grad/Shape, gradients/Sub_grad/Shape_1)]]
  
  Caused by op u'gradients/Sub_grad/BroadcastGradientArgs', defined at:
    File "conv_rnn_experiment.py", line 63, in &lt;module&gt;
      optimise = tf.train.AdamOptimizer(1e-4).minimize(rmse)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 269, in minimize
      grad_loss=grad_loss)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 335, in compute_gradients
      colocate_gradients_with_ops=colocate_gradients_with_ops)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients_impl.py", line 482, in gradients
      in_grads = grad_fn(op, *out_grads)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_grad.py", line 594, in _SubGrad
      rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 390, in _broadcast_gradient_args
      name=name)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
      op_def=op_def)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
      original_op=self._default_original_op, op_def=op_def)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
      self._traceback = _extract_stack()
  
    ...which was originally created as op u'Sub', defined at:
    File "conv_rnn_experiment.py", line 62, in &lt;module&gt;
      rmse = error(y, prediction)
    File "conv_rnn_experiment.py", line 60, in error
      return tf.sqrt(tf.reduce_mean(tf.square(tf.sub(labels, prediction))))
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 2758, in sub
      result = _op_def_lib.apply_op("Sub", x=x, y=y, name=name)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
      op_def=op_def)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
      original_op=self._default_original_op, op_def=op_def)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
      self._traceback = _extract_stack()
  
    InvalidArgumentError (see above for traceback): Incompatible shapes: [100,155] vs. [128,155]
       [[Node: gradients/Sub_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"](gradients/Sub_grad/Shape, gradients/Sub_grad/Shape_1)]]
  </code></pre>