mirror of
https://github.com/wassname/keras-language-modeling.git
synced 2026-09-11 12:20:57 +08:00
171 lines
7.4 KiB
Python
171 lines
7.4 KiB
Python
from __future__ import absolute_import
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from keras import backend as K
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from keras.engine import InputSpec
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from keras.layers import LSTM, activations, Wrapper
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class AttentionLSTM(LSTM):
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def __init__(self, output_dim, attention_vec, attn_activation='tanh', single_attention_param=False, **kwargs):
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self.attention_vec = attention_vec
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self.attn_activation = activations.get(attn_activation)
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self.single_attention_param = single_attention_param
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super(AttentionLSTM, self).__init__(output_dim, **kwargs)
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def build(self, input_shape):
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super(AttentionLSTM, self).build(input_shape)
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if hasattr(self.attention_vec, '_keras_shape'):
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attention_dim = self.attention_vec._keras_shape[1]
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else:
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raise Exception('Layer could not be build: No information about expected input shape.')
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self.U_a = self.inner_init((self.output_dim, self.output_dim),
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name='{}_U_a'.format(self.name))
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self.b_a = K.zeros((self.output_dim,), name='{}_b_a'.format(self.name))
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self.U_m = self.inner_init((attention_dim, self.output_dim),
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name='{}_U_m'.format(self.name))
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self.b_m = K.zeros((self.output_dim,), name='{}_b_m'.format(self.name))
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if self.single_attention_param:
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self.U_s = self.inner_init((self.output_dim, 1),
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name='{}_U_s'.format(self.name))
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self.b_s = K.zeros((1,), name='{}_b_s'.format(self.name))
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else:
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self.U_s = self.inner_init((self.output_dim, self.output_dim),
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name='{}_U_s'.format(self.name))
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self.b_s = K.zeros((self.output_dim,), name='{}_b_s'.format(self.name))
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self.trainable_weights += [self.U_a, self.U_m, self.U_s, self.b_a, self.b_m, self.b_s]
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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def step(self, x, states):
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h, [h, c] = super(AttentionLSTM, self).step(x, states)
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attention = states[4]
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m = self.attn_activation(K.dot(h, self.U_a) * attention + self.b_a)
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# Intuitively it makes more sense to use a sigmoid (was getting some NaN problems
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# which I think might have been caused by the exponential function -> gradients blow up)
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s = K.sigmoid(K.dot(m, self.U_s) + self.b_s)
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if self.single_attention_param:
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h = h * K.repeat_elements(s, self.output_dim, axis=1)
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else:
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h = h * s
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return h, [h, c]
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def get_constants(self, x):
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constants = super(AttentionLSTM, self).get_constants(x)
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constants.append(K.dot(self.attention_vec, self.U_m) + self.b_m)
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return constants
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class AttentionLSTMWrapper(Wrapper):
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def __init__(self, layer, attention_vec, attn_activation='tanh', single_attention_param=False, **kwargs):
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assert isinstance(layer, LSTM)
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self.supports_masking = True
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self.attention_vec = attention_vec
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self.attn_activation = activations.get(attn_activation)
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self.single_attention_param = single_attention_param
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super(AttentionLSTMWrapper, self).__init__(layer, **kwargs)
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def build(self, input_shape):
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assert len(input_shape) >= 3
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self.input_spec = [InputSpec(shape=input_shape)]
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if not self.layer.built:
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self.layer.build(input_shape)
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self.layer.built = True
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super(AttentionLSTMWrapper, self).build()
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if hasattr(self.attention_vec, '_keras_shape'):
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attention_dim = self.attention_vec._keras_shape[1]
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else:
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raise Exception('Layer could not be build: No information about expected input shape.')
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self.U_a = self.layer.inner_init((self.layer.output_dim, self.layer.output_dim), name='{}_U_a'.format(self.name))
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self.b_a = K.zeros((self.layer.output_dim,), name='{}_b_a'.format(self.name))
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self.U_m = self.layer.inner_init((attention_dim, self.layer.output_dim), name='{}_U_m'.format(self.name))
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self.b_m = K.zeros((self.layer.output_dim,), name='{}_b_m'.format(self.name))
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if self.single_attention_param:
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self.U_s = self.layer.inner_init((self.layer.output_dim, 1), name='{}_U_s'.format(self.name))
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self.b_s = K.zeros((1,), name='{}_b_s'.format(self.name))
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else:
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self.U_s = self.layer.inner_init((self.layer.output_dim, self.layer.output_dim), name='{}_U_s'.format(self.name))
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self.b_s = K.zeros((self.layer.output_dim,), name='{}_b_s'.format(self.name))
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self.trainable_weights = [self.U_a, self.U_m, self.U_s, self.b_a, self.b_m, self.b_s]
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def get_output_shape_for(self, input_shape):
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return self.layer.get_output_shape_for(input_shape)
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def step(self, x, states):
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h, [h, c] = self.layer.step(x, states)
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attention = states[4]
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m = self.attn_activation(K.dot(h, self.U_a) * attention + self.b_a)
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s = K.sigmoid(K.dot(m, self.U_s) + self.b_s)
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if self.single_attention_param:
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h = h * K.repeat_elements(s, self.layer.output_dim, axis=1)
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else:
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h = h * s
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return h, [h, c]
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def get_constants(self, x):
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constants = self.layer.get_constants(x)
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constants.append(K.dot(self.attention_vec, self.U_m) + self.b_m)
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return constants
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def call(self, x, mask=None):
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# input shape: (nb_samples, time (padded with zeros), input_dim)
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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.layer.stateful:
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initial_states = self.layer.states
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else:
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initial_states = self.layer.get_initial_states(x)
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constants = self.get_constants(x)
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preprocessed_input = self.layer.preprocess_input(x)
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last_output, outputs, states = K.rnn(self.step, preprocessed_input,
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initial_states,
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go_backwards=self.layer.go_backwards,
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mask=mask,
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constants=constants,
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unroll=self.layer.unroll,
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input_length=input_shape[1])
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if self.layer.stateful:
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self.updates = []
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for i in range(len(states)):
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self.updates.append((self.layer.states[i], states[i]))
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if self.layer.return_sequences:
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return outputs
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else:
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return last_output
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