TensorFlow: Incompatible shapes: [100,155] vs. [128,155] when combining CNN and LSTM

Problem

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?

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)

Error

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 <module> 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 <module> 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 <module> 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)]]

Answer1:

If you print a couple things before when you do your sess.run() you'll notice that it breaks at lstm_output. Going off that, you can start narrowing down your issue, which ended up being this line:

init_state = lstm_cell.zero_state(128, tf.float32)

This initialization is to determine the batch size. Since you have 155 units and declared a batch size of 128, it's expected that the input is 128 x 155. However, it seems like your batch is 100, so if you change that line it should work.

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