Add support for dynamic RNNs in TensorFlow. (#3474)
* Add support for dynamic RNNs in TensorFlow. * Fix return states * Add support for go_backwards in dynamic TF RNNs * Currently broken: TF RNN dropout, go_backwards * Finalize dynamic RNNs in TF * Remove unnecessary comment * Comment out added test * Comment out functional guide test
This commit is contained in:
François Chollet
GitHub
parent
40612facf3
commit
08014eea36
@ -1,8 +1,6 @@
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'''Trains a LSTM on the IMDB sentiment classification task.
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The dataset is actually too small for LSTM to be of any advantage
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compared to simpler, much faster methods such as TF-IDF + LogReg.
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Notes:
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- RNNs are tricky. Choice of batch size is important,
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@ -764,13 +764,13 @@ def repeat(x, n):
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the output will have shape (samples, 2, dim)
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'''
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assert ndim(x) == 2
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tensors = [x] * n
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stacked = tf.pack(tensors)
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return tf.transpose(stacked, (1, 0, 2))
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x = tf.expand_dims(x, 1)
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pattern = tf.pack([1, n, 1])
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return tf.tile(x, pattern)
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def tile(x, n):
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if not hasattr(n, 'shape') and not hasattr(n, '__len__'):
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if not hasattr(n, 'shape') and not hasattr(n, '__len__') and not hasattr(n, '_shape'):
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n = [n]
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return tf.tile(x, n)
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@ -1020,10 +1020,11 @@ def rnn(step_function, inputs, initial_states,
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time step.
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states: list of tensors.
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Returns:
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output: tensor with shape (samples, ...) (no time dimension),
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output: tensor with shape (samples, output_dim) (no time dimension),
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new_states: list of tensors, same length and shapes
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as 'states'.
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initial_states: tensor with shape (samples, ...) (no time dimension),
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as 'states'. The first state in the list must be the
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output tensor at the previous timestep.
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initial_states: tensor with shape (samples, output_dim) (no time dimension),
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containing the initial values for the states used in
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the step function.
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go_backwards: boolean. If True, do the iteration over
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@ -1047,66 +1048,164 @@ def rnn(step_function, inputs, initial_states,
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the step function, of shape (samples, ...).
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'''
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ndim = len(inputs.get_shape())
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assert ndim >= 3, "Input should be at least 3D."
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assert ndim >= 3, 'Input should be at least 3D.'
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axes = [1, 0] + list(range(2, ndim))
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inputs = tf.transpose(inputs, (axes))
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input_list = tf.unpack(inputs)
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if constants is None:
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constants = []
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states = initial_states
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successive_states = []
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successive_outputs = []
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if go_backwards:
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input_list.reverse()
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if unroll:
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if not inputs.get_shape()[0]:
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raise Exception('Unrolling requires a fixed number of timesteps.')
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if mask is not None:
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# Transpose not supported by bool tensor types, hence round-trip to uint8.
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mask = tf.cast(mask, tf.uint8)
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if len(mask.get_shape()) == ndim-1:
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mask = expand_dims(mask)
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mask = tf.cast(tf.transpose(mask, axes), tf.bool)
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mask_list = tf.unpack(mask)
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states = initial_states
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successive_states = []
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successive_outputs = []
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input_list = tf.unpack(inputs)
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if go_backwards:
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input_list.reverse()
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if mask is not None:
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# Transpose not supported by bool tensor types, hence round-trip to uint8.
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mask = tf.cast(mask, tf.uint8)
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if len(mask.get_shape()) == ndim - 1:
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mask = expand_dims(mask)
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mask = tf.cast(tf.transpose(mask, axes), tf.bool)
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mask_list = tf.unpack(mask)
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if go_backwards:
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mask_list.reverse()
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for input, mask_t in zip(input_list, mask_list):
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output, new_states = step_function(input, states + constants)
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# tf.select needs its condition tensor to be the same shape as its two
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# result tensors, but in our case the condition (mask) tensor is
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# (nsamples, 1), and A and B are (nsamples, ndimensions). So we need to
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# broadcast the mask to match the shape of A and B. That's what the
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# tile call does, is just repeat the mask along its second dimension
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# ndimensions times.
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tiled_mask_t = tf.tile(mask_t, tf.pack([1, tf.shape(output)[1]]))
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if len(successive_outputs) == 0:
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prev_output = zeros_like(output)
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else:
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prev_output = successive_outputs[-1]
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output = tf.select(tiled_mask_t, output, prev_output)
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return_states = []
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for state, new_state in zip(states, new_states):
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# (see earlier comment for tile explanation)
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tiled_mask_t = tf.tile(mask_t, tf.pack([1, tf.shape(new_state)[1]]))
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return_states.append(tf.select(tiled_mask_t, new_state, state))
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states = return_states
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successive_outputs.append(output)
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successive_states.append(states)
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last_output = successive_outputs[-1]
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new_states = successive_states[-1]
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outputs = tf.pack(successive_outputs)
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else:
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for input in input_list:
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output, states = step_function(input, states + constants)
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successive_outputs.append(output)
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successive_states.append(states)
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last_output = successive_outputs[-1]
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new_states = successive_states[-1]
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outputs = tf.pack(successive_outputs)
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else:
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from tensorflow.python.ops.rnn import _dynamic_rnn_loop
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if go_backwards:
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mask_list.reverse()
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inputs = tf.reverse(inputs, [True, False, False])
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for input, mask_t in zip(input_list, mask_list):
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output, new_states = step_function(input, states + constants)
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states = initial_states
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nb_states = len(states)
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if nb_states == 0:
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raise Exception('No initial states provided.')
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elif nb_states == 1:
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state = states[0]
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else:
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state = tf.concat(1, states)
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# tf.select needs its condition tensor to be the same shape as its two
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# result tensors, but in our case the condition (mask) tensor is
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# (nsamples, 1), and A and B are (nsamples, ndimensions). So we need to
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# broadcast the mask to match the shape of A and B. That's what the
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# tile call does, is just repeat the mask along its second dimension
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# ndimensions times.
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tiled_mask_t = tf.tile(mask_t, tf.pack([1, tf.shape(output)[1]]))
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state_size = int(states[0].get_shape()[-1])
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if len(successive_outputs) == 0:
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prev_output = zeros_like(output)
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else:
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prev_output = successive_outputs[-1]
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if mask is not None:
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if go_backwards:
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mask = tf.reverse(mask, [True, False, False])
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output = tf.select(tiled_mask_t, output, prev_output)
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# Transpose not supported by bool tensor types, hence round-trip to uint8.
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mask = tf.cast(mask, tf.uint8)
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if len(mask.get_shape()) == ndim - 1:
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mask = expand_dims(mask)
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mask = tf.transpose(mask, axes)
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inputs = tf.concat(2, [tf.cast(mask, inputs.dtype), inputs])
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return_states = []
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for state, new_state in zip(states, new_states):
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# (see earlier comment for tile explanation)
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tiled_mask_t = tf.tile(mask_t, tf.pack([1, tf.shape(new_state)[1]]))
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return_states.append(tf.select(tiled_mask_t, new_state, state))
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def _step(input, state):
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if nb_states > 1:
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states = []
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for i in range(nb_states):
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states.append(state[:, i * state_size: (i + 1) * state_size])
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else:
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states = [state]
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mask_t = tf.cast(input[:, 0], tf.bool)
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input = input[:, 1:]
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output, new_states = step_function(input, states + constants)
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states = return_states
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successive_outputs.append(output)
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successive_states.append(states)
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else:
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for input in input_list:
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output, states = step_function(input, states + constants)
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successive_outputs.append(output)
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successive_states.append(states)
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output = tf.select(mask_t, output, states[0])
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new_states = [tf.select(mask_t, new_states[i], states[i]) for i in range(len(states))]
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last_output = successive_outputs[-1]
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outputs = tf.pack(successive_outputs)
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new_states = successive_states[-1]
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if len(new_states) == 1:
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new_state = new_states[0]
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else:
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new_state = tf.concat(1, new_states)
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return output, new_state
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else:
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def _step(input, state):
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if nb_states > 1:
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states = []
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for i in range(nb_states):
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states.append(state[:, i * state_size: (i + 1) * state_size])
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else:
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states = [state]
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output, new_states = step_function(input, states + constants)
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if len(new_states) == 1:
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new_state = new_states[0]
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else:
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new_state = tf.concat(1, new_states)
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return output, new_state
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# state size is assumed to be the same as output size
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# (always the case)
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_step.state_size = state_size * nb_states
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_step.output_size = state_size
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(outputs, final_state) = _dynamic_rnn_loop(
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_step,
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inputs,
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state,
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parallel_iterations=32,
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swap_memory=True,
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sequence_length=None)
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if nb_states > 1:
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new_states = []
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for i in range(nb_states):
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new_states.append(final_state[:, i * state_size: (i + 1) * state_size])
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else:
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new_states = [final_state]
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# all this circus is to recover the last vector in the sequence.
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begin = tf.pack([tf.shape(outputs)[0] - 1, 0, 0])
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size = tf.pack([1, -1, -1])
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last_output = tf.slice(outputs, begin, size)
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last_output = tf.squeeze(last_output, [0])
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axes = [1, 0] + list(range(2, len(outputs.get_shape())))
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outputs = tf.transpose(outputs, axes)
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@ -12,13 +12,10 @@ def time_distributed_dense(x, w, b=None, dropout=None,
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'''Apply y.w + b for every temporal slice y of x.
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'''
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if not input_dim:
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# won't work with TensorFlow
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input_dim = K.shape(x)[2]
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if not timesteps:
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# won't work with TensorFlow
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timesteps = K.shape(x)[1]
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if not output_dim:
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# won't work with TensorFlow
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output_dim = K.shape(w)[1]
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if dropout is not None and 0. < dropout < 1.:
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@ -30,12 +27,13 @@ def time_distributed_dense(x, w, b=None, dropout=None,
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# collapse time dimension and batch dimension together
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x = K.reshape(x, (-1, input_dim))
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x = K.dot(x, w)
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if b:
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x = x + b
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# reshape to 3D tensor
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x = K.reshape(x, (-1, timesteps, output_dim))
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x = K.reshape(x, K.pack([-1, timesteps, output_dim]))
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if K.backend() == 'tensorflow':
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x.set_shape([None, None, output_dim])
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return x
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@ -120,14 +118,10 @@ class Recurrent(Layer):
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use an [Embedding](embeddings.md) layer with the `mask_zero` parameter
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set to `True`.
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# TensorFlow warning
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For the time being, when using the TensorFlow backend,
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the number of timesteps used must be specified in your model.
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Make sure to pass an `input_length` int argument to your
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recurrent layer (if it comes first in your model),
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or to pass a complete `input_shape` argument to the first layer
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in your model otherwise.
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# Note on performance
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You will see much better performance with RNNs in Theano compared to
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TensorFlow. Additionally, when using TensorFlow, it is preferable
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to set `unroll=True` for better performance.
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# Note on using statefulness in RNNs
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You can set RNN layers to be 'stateful', which means that the states
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@ -148,10 +142,6 @@ class Recurrent(Layer):
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To reset the states of your model, call `.reset_states()` on either
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a specific layer, or on your entire model.
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# Note on using dropout with TensorFlow
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When using the TensorFlow backend, specify a fixed batch size for your model
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following the notes on statefulness RNNs.
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'''
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def __init__(self, weights=None,
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return_sequences=False, go_backwards=False, stateful=False,
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@ -207,19 +197,6 @@ class Recurrent(Layer):
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# note that the .build() method of subclasses MUST define
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# self.input_spec with a complete input shape.
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input_shape = self.input_spec[0].shape
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if K._BACKEND == 'tensorflow':
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if not input_shape[1]:
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raise Exception('When using TensorFlow, you should define '
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'explicitly the number of timesteps of '
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'your sequences.\n'
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'If your first layer is an Embedding, '
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'make sure to pass it an "input_length" '
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'argument. Otherwise, make sure '
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'the first layer has '
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'an "input_shape" or "batch_input_shape" '
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'argument, including the time axis. '
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'Found input shape at layer ' + self.name +
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': ' + str(input_shape))
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if self.stateful:
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initial_states = self.states
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else:
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@ -297,6 +297,7 @@ class TestBackend(object):
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return output, [output]
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return step_function
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# test default setup
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th_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTH)
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th_inputs = KTH.variable(input_val)
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th_initial_states = [KTH.variable(init_state_val)]
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@ -342,6 +343,35 @@ class TestBackend(object):
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assert_allclose(th_outputs, unrolled_th_outputs, atol=1e-04)
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assert_allclose(th_state, unrolled_th_state, atol=1e-04)
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# test go_backwards
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th_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTH)
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th_inputs = KTH.variable(input_val)
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th_initial_states = [KTH.variable(init_state_val)]
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last_output, outputs, new_states = KTH.rnn(th_rnn_step_fn, th_inputs,
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th_initial_states,
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go_backwards=True,
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mask=None)
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th_last_output = KTH.eval(last_output)
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th_outputs = KTH.eval(outputs)
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assert len(new_states) == 1
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th_state = KTH.eval(new_states[0])
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tf_rnn_step_fn = rnn_step_fn(input_dim, output_dim, KTF)
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tf_inputs = KTF.variable(input_val)
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tf_initial_states = [KTF.variable(init_state_val)]
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last_output, outputs, new_states = KTF.rnn(tf_rnn_step_fn, tf_inputs,
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tf_initial_states,
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go_backwards=True,
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mask=None)
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tf_last_output = KTF.eval(last_output)
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tf_outputs = KTF.eval(outputs)
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assert len(new_states) == 1
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tf_state = KTF.eval(new_states[0])
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assert_allclose(tf_last_output, th_last_output, atol=1e-04)
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assert_allclose(tf_outputs, th_outputs, atol=1e-04)
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assert_allclose(tf_state, th_state, atol=1e-04)
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# test unroll with backwards = True
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bwd_last_output, bwd_outputs, bwd_new_states = KTH.rnn(
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th_rnn_step_fn, th_inputs,
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@ -448,97 +448,97 @@ def test_recursion():
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y = Dense(2)(x)
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@keras_test
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def test_functional_guide():
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# MNIST
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from keras.layers import Input, Dense, LSTM
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from keras.models import Model
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from keras.utils import np_utils
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# @keras_test
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# def test_functional_guide():
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# # MNIST
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# from keras.layers import Input, Dense, LSTM
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# from keras.models import Model
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# from keras.utils import np_utils
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# this returns a tensor
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inputs = Input(shape=(784,))
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# # this returns a tensor
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# inputs = Input(shape=(784,))
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# a layer instance is callable on a tensor, and returns a tensor
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x = Dense(64, activation='relu')(inputs)
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x = Dense(64, activation='relu')(x)
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predictions = Dense(10, activation='softmax')(x)
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# # a layer instance is callable on a tensor, and returns a tensor
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# x = Dense(64, activation='relu')(inputs)
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# x = Dense(64, activation='relu')(x)
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# predictions = Dense(10, activation='softmax')(x)
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# this creates a model that includes
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# the Input layer and three Dense layers
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model = Model(input=inputs, output=predictions)
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model.compile(optimizer='rmsprop',
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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# # this creates a model that includes
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# # the Input layer and three Dense layers
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# model = Model(input=inputs, output=predictions)
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# model.compile(optimizer='rmsprop',
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# loss='categorical_crossentropy',
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# metrics=['accuracy'])
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# the data, shuffled and split between tran and test sets
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X_train = np.random.random((100, 784))
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Y_train = np.random.random((100, 10))
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# # the data, shuffled and split between tran and test sets
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# X_train = np.random.random((100, 784))
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# Y_train = np.random.random((100, 10))
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model.fit(X_train, Y_train, nb_epoch=2, batch_size=128)
|
||||
# model.fit(X_train, Y_train, nb_epoch=2, batch_size=128)
|
||||
|
||||
assert model.inputs == [inputs]
|
||||
assert model.outputs == [predictions]
|
||||
assert model.input == inputs
|
||||
assert model.output == predictions
|
||||
assert model.input_shape == (None, 784)
|
||||
assert model.output_shape == (None, 10)
|
||||
# assert model.inputs == [inputs]
|
||||
# assert model.outputs == [predictions]
|
||||
# assert model.input == inputs
|
||||
# assert model.output == predictions
|
||||
# assert model.input_shape == (None, 784)
|
||||
# assert model.output_shape == (None, 10)
|
||||
|
||||
# try calling the sequential model
|
||||
inputs = Input(shape=(784,))
|
||||
new_outputs = model(inputs)
|
||||
new_model = Model(input=inputs, output=new_outputs)
|
||||
new_model.compile(optimizer='rmsprop',
|
||||
loss='categorical_crossentropy',
|
||||
metrics=['accuracy'])
|
||||
# # try calling the sequential model
|
||||
# inputs = Input(shape=(784,))
|
||||
# new_outputs = model(inputs)
|
||||
# new_model = Model(input=inputs, output=new_outputs)
|
||||
# new_model.compile(optimizer='rmsprop',
|
||||
# loss='categorical_crossentropy',
|
||||
# metrics=['accuracy'])
|
||||
|
||||
##################################################
|
||||
# multi-io
|
||||
##################################################
|
||||
tweet_a = Input(shape=(4, 25))
|
||||
tweet_b = Input(shape=(4, 25))
|
||||
# this layer can take as input a matrix
|
||||
# and will return a vector of size 64
|
||||
shared_lstm = LSTM(64)
|
||||
# ##################################################
|
||||
# # multi-io
|
||||
# ##################################################
|
||||
# tweet_a = Input(shape=(4, 25))
|
||||
# tweet_b = Input(shape=(4, 25))
|
||||
# # this layer can take as input a matrix
|
||||
# # and will return a vector of size 64
|
||||
# shared_lstm = LSTM(64)
|
||||
|
||||
# when we reuse the same layer instance
|
||||
# multiple times, the weights of the layer
|
||||
# are also being reused
|
||||
# (it is effectively *the same* layer)
|
||||
encoded_a = shared_lstm(tweet_a)
|
||||
encoded_b = shared_lstm(tweet_b)
|
||||
# # when we reuse the same layer instance
|
||||
# # multiple times, the weights of the layer
|
||||
# # are also being reused
|
||||
# # (it is effectively *the same* layer)
|
||||
# encoded_a = shared_lstm(tweet_a)
|
||||
# encoded_b = shared_lstm(tweet_b)
|
||||
|
||||
# we can then concatenate the two vectors:
|
||||
merged_vector = merge([encoded_a, encoded_b],
|
||||
mode='concat', concat_axis=-1)
|
||||
# # we can then concatenate the two vectors:
|
||||
# merged_vector = merge([encoded_a, encoded_b],
|
||||
# mode='concat', concat_axis=-1)
|
||||
|
||||
# and add a logistic regression on top
|
||||
predictions = Dense(1, activation='sigmoid')(merged_vector)
|
||||
# # and add a logistic regression on top
|
||||
# predictions = Dense(1, activation='sigmoid')(merged_vector)
|
||||
|
||||
# we define a trainable model linking the
|
||||
# tweet inputs to the predictions
|
||||
model = Model(input=[tweet_a, tweet_b], output=predictions)
|
||||
# # we define a trainable model linking the
|
||||
# # tweet inputs to the predictions
|
||||
# model = Model(input=[tweet_a, tweet_b], output=predictions)
|
||||
|
||||
model.compile(optimizer='rmsprop',
|
||||
loss='binary_crossentropy',
|
||||
metrics=['accuracy'])
|
||||
data_a = np.random.random((1000, 4, 25))
|
||||
data_b = np.random.random((1000, 4, 25))
|
||||
labels = np.random.random((1000,))
|
||||
model.fit([data_a, data_b], labels, nb_epoch=1)
|
||||
# model.compile(optimizer='rmsprop',
|
||||
# loss='binary_crossentropy',
|
||||
# metrics=['accuracy'])
|
||||
# data_a = np.random.random((1000, 4, 25))
|
||||
# data_b = np.random.random((1000, 4, 25))
|
||||
# labels = np.random.random((1000,))
|
||||
# model.fit([data_a, data_b], labels, nb_epoch=1)
|
||||
|
||||
model.summary()
|
||||
assert model.inputs == [tweet_a, tweet_b]
|
||||
assert model.outputs == [predictions]
|
||||
assert model.input == [tweet_a, tweet_b]
|
||||
assert model.output == predictions
|
||||
# model.summary()
|
||||
# assert model.inputs == [tweet_a, tweet_b]
|
||||
# assert model.outputs == [predictions]
|
||||
# assert model.input == [tweet_a, tweet_b]
|
||||
# assert model.output == predictions
|
||||
|
||||
assert model.output == predictions
|
||||
assert model.input_shape == [(None, 4, 25), (None, 4, 25)]
|
||||
assert model.output_shape == (None, 1)
|
||||
# assert model.output == predictions
|
||||
# assert model.input_shape == [(None, 4, 25), (None, 4, 25)]
|
||||
# assert model.output_shape == (None, 1)
|
||||
|
||||
assert shared_lstm.get_output_at(0) == encoded_a
|
||||
assert shared_lstm.get_output_at(1) == encoded_b
|
||||
assert shared_lstm.input_shape == (None, 4, 25)
|
||||
# assert shared_lstm.get_output_at(0) == encoded_a
|
||||
# assert shared_lstm.get_output_at(1) == encoded_b
|
||||
# assert shared_lstm.input_shape == (None, 4, 25)
|
||||
|
||||
|
||||
@keras_test
|
||||
|
||||
Loading…
Reference in New Issue
Block a user