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attentive_convlstm.py
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attentive_convlstm.py
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from __future__ import division
import keras.backend as K
from keras.layers import Layer, InputSpec
from keras.layers.convolutional import Convolution2D
from keras import initializers, activations
class AttentiveConvLSTM(Layer):
def __init__(self, nb_filters_in, nb_filters_out, nb_filters_att, nb_rows, nb_cols,
init='normal', inner_init='orthogonal', attentive_init='zero',
activation='tanh', inner_activation='sigmoid',
W_regularizer=None, U_regularizer=None,
weights=None, go_backwards=False,
**kwargs):
self.nb_filters_in = nb_filters_in
self.nb_filters_out = nb_filters_out
self.nb_filters_att = nb_filters_att
self.nb_rows = nb_rows
self.nb_cols = nb_cols
self.init = initializers.get(init)
self.inner_init = initializers.get(inner_init)
self.attentive_init = initializers.get(attentive_init)
self.activation = activations.get(activation)
self.inner_activation = activations.get(inner_activation)
self.initial_weights = weights
self.go_backwards = go_backwards
self.W_regularizer = W_regularizer
self.U_regularizer = U_regularizer
self.input_spec = [InputSpec(ndim=5)]
super(AttentiveConvLSTM, self).__init__(**kwargs)
def get_output_shape_for(self, input_shape):
return input_shape[:1] + (self.nb_filters_out,) + input_shape[3:]
def compute_mask(self, input, mask):
return None
def get_initial_states(self, x):
initial_state = K.sum(x, axis=1)
print('what is this: ', (self.nb_filters_out, self.nb_filters_in, 1, 1))
initial_state = K.conv2d(initial_state, K.zeros((self.nb_filters_out, self.nb_filters_in, 1, 1)), padding='same')
initial_states = [initial_state for _ in range(len(self.states))]
return initial_states
def build(self, input_shape):
self.input_spec = [InputSpec(shape=input_shape)]
self.states = [None, None]
self.trainable_weights = []
self.W_a = Convolution2D(self.nb_filters_att, self.nb_rows, self.nb_cols, border_mode='same', bias=True, init=self.init)
self.U_a = Convolution2D(self.nb_filters_att, self.nb_rows, self.nb_cols, border_mode='same', bias=True, init=self.init)
self.V_a = Convolution2D(1, self.nb_rows, self.nb_cols, border_mode='same', bias=False, init=self.attentive_init)
self.W_a.build((input_shape[0], self.nb_filters_att, input_shape[3], input_shape[4]))
self.U_a.build((input_shape[0], self.nb_filters_in, input_shape[3], input_shape[4]))
self.V_a.build((input_shape[0], self.nb_filters_att, input_shape[3], input_shape[4]))
self.W_a.built = True
self.U_a.built = True
self.V_a.built = True
self.W_i = Convolution2D(self.nb_filters_out, self.nb_rows, self.nb_cols, border_mode='same', bias=True, init=self.init)
self.U_i = Convolution2D(self.nb_filters_out, self.nb_rows, self.nb_cols, border_mode='same', bias=True, init=self.inner_init)
self.W_i.build((input_shape[0], self.nb_filters_in, input_shape[3], input_shape[4]))
self.U_i.build((input_shape[0], self.nb_filters_out, input_shape[3], input_shape[4]))
self.W_i.built = True
self.U_i.built = True
self.W_f = Convolution2D(self.nb_filters_out, self.nb_rows, self.nb_cols, border_mode='same', bias=True, init=self.init)
self.U_f = Convolution2D(self.nb_filters_out, self.nb_rows, self.nb_cols, border_mode='same', bias=True, init=self.inner_init)
self.W_f.build((input_shape[0], self.nb_filters_in, input_shape[3], input_shape[4]))
self.U_f.build((input_shape[0], self.nb_filters_out, input_shape[3], input_shape[4]))
self.W_f.built = True
self.U_f.built = True
self.W_c = Convolution2D(self.nb_filters_out, self.nb_rows, self.nb_cols, border_mode='same', bias=True, init=self.init)
self.U_c = Convolution2D(self.nb_filters_out, self.nb_rows, self.nb_cols, border_mode='same', bias=True, init=self.inner_init)
self.W_c.build((input_shape[0], self.nb_filters_in, input_shape[3], input_shape[4]))
self.U_c.build((input_shape[0], self.nb_filters_out, input_shape[3], input_shape[4]))
self.W_c.built = True
self.U_c.built = True
self.W_o = Convolution2D(self.nb_filters_out, self.nb_rows, self.nb_cols, border_mode='same', bias=True, init=self.init)
self.U_o = Convolution2D(self.nb_filters_out, self.nb_rows, self.nb_cols, border_mode='same', bias=True, init=self.inner_init)
self.W_o.build((input_shape[0], self.nb_filters_in, input_shape[3], input_shape[4]))
self.U_o.build((input_shape[0], self.nb_filters_out, input_shape[3], input_shape[4]))
self.W_o.built = True
self.U_o.built = True
self.trainable_weights = []
self.trainable_weights.extend(self.W_a.trainable_weights)
self.trainable_weights.extend(self.U_a.trainable_weights)
self.trainable_weights.extend(self.V_a.trainable_weights)
self.trainable_weights.extend(self.W_i.trainable_weights)
self.trainable_weights.extend(self.U_i.trainable_weights)
self.trainable_weights.extend(self.W_f.trainable_weights)
self.trainable_weights.extend(self.U_f.trainable_weights)
self.trainable_weights.extend(self.W_c.trainable_weights)
self.trainable_weights.extend(self.U_c.trainable_weights)
self.trainable_weights.extend(self.W_o.trainable_weights)
self.trainable_weights.extend(self.U_o.trainable_weights)
def preprocess_input(self, x):
return x
def step(self, x, states):
x_shape = K.shape(x)
h_tm1 = states[0]
c_tm1 = states[1]
e = self.V_a(K.tanh(self.W_a(h_tm1) + self.U_a(x)))
a = K.reshape(K.softmax(K.batch_flatten(e)), (x_shape[0], 1, x_shape[2], x_shape[3]))
x_tilde = x * K.repeat_elements(a, x_shape[1], 1)
x_i = self.W_i(x_tilde)
x_f = self.W_f(x_tilde)
x_c = self.W_c(x_tilde)
x_o = self.W_o(x_tilde)
i = self.inner_activation(x_i + self.U_i(h_tm1))
f = self.inner_activation(x_f + self.U_f(h_tm1))
c = f * c_tm1 + i * self.activation(x_c + self.U_c(h_tm1))
o = self.inner_activation(x_o + self.U_o(h_tm1))
h = o * self.activation(c)
return h, [h, c]
def get_constants(self, x):
return []
def call(self, x, mask=None):
input_shape = self.input_spec[0].shape
initial_states = self.get_initial_states(x)
constants = self.get_constants(x)
preprocessed_input = self.preprocess_input(x)
last_output, outputs, states = K.rnn(self.step, preprocessed_input,
initial_states,
go_backwards=False,
mask=mask,
constants=constants,
unroll=False,
input_length=input_shape[1])
if last_output.ndim == 3:
last_output = K.expand_dims(last_output, dim=0)
return last_output