From 83009bc354890358ae0a6ca5b8752e64789317b2 Mon Sep 17 00:00:00 2001
From: Jurica Seva <seva@informatik.hu-berlin.de>
Date: Thu, 21 Jun 2018 09:35:27 +0200
Subject: [PATCH] My changes
---
code_jurica/_layers.py | 350 +++-
code_jurica/attentionDecoder.py | 361 ++++
code_jurica/loader.py | 6 +-
.../logs/classification_report_test.csv | 1556 +++++++----------
code_jurica/seq2seq_attention.py | 30 +-
code_jurica/train.sh | 2 +-
6 files changed, 1379 insertions(+), 926 deletions(-)
create mode 100644 code_jurica/attentionDecoder.py
diff --git a/code_jurica/_layers.py b/code_jurica/_layers.py
index e5befe1..874a99f 100644
--- a/code_jurica/_layers.py
+++ b/code_jurica/_layers.py
@@ -10,7 +10,7 @@ Author: Xifeng Guo, E-mail: `guoxifeng1990@163.com`, Github: `https://github.com
import tensorflow as tf
from keras import backend as K, initializers, regularizers, constraints, layers
from keras.engine.topology import Layer
-from keras.layers import InputSpec
+from keras.layers import InputSpec, Recurrent
# https://gist.github.com/cbaziotis/7ef97ccf71cbc14366835198c09809d2
# https://github.com/cbaziotis/hierarchical-rnn-biocreative-4/blob/master/models/nn_models.py
@@ -378,4 +378,350 @@ class AttentionWithContext(Layer):
def compute_output_shape(self, input_shape):
- return (input_shape[0], input_shape[-1],)
\ No newline at end of file
+ return (input_shape[0], input_shape[-1],)
+
+# ATTENTION DECODER
+# https://github.com/datalogue/keras-attention
+
+def _time_distributed_dense(x, w, b=None, dropout=None,
+ input_dim=None, output_dim=None,
+ timesteps=None, training=None):
+ """Apply `y . w + b` for every temporal slice y of x.
+ # Arguments
+ x: input tensor.
+ w: weight matrix.
+ b: optional bias vector.
+ dropout: wether to apply dropout (same dropout mask
+ for every temporal slice of the input).
+ input_dim: integer; optional dimensionality of the input.
+ output_dim: integer; optional dimensionality of the output.
+ timesteps: integer; optional number of timesteps.
+ training: training phase tensor or boolean.
+ # Returns
+ Output tensor.
+ """
+ if not input_dim:
+ input_dim = K.shape(x)[2]
+ if not timesteps:
+ timesteps = K.shape(x)[1]
+ if not output_dim:
+ output_dim = K.shape(w)[1]
+
+ if dropout is not None and 0. < dropout < 1.:
+ # apply the same dropout pattern at every timestep
+ ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim)))
+ dropout_matrix = K.dropout(ones, dropout)
+ expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps)
+ x = K.in_train_phase(x * expanded_dropout_matrix, x, training=training)
+
+ # collapse time dimension and batch dimension together
+ x = K.reshape(x, (-1, input_dim))
+ x = K.dot(x, w)
+ if b is not None:
+ x = K.bias_add(x, b)
+ # reshape to 3D tensor
+ if K.backend() == 'tensorflow':
+ x = K.reshape(x, K.stack([-1, timesteps, output_dim]))
+ x.set_shape([None, None, output_dim])
+ else:
+ x = K.reshape(x, (-1, timesteps, output_dim))
+ return x
+
+tfPrint = lambda d, T: tf.Print(input_=T, data=[T, tf.shape(T)], message=d)
+
+class AttentionDecoder(Recurrent):
+
+ def __init__(self, units, output_dim,
+ activation='tanh',
+ return_probabilities=False,
+ name='AttentionDecoder',
+ kernel_initializer='glorot_uniform',
+ recurrent_initializer='orthogonal',
+ bias_initializer='zeros',
+ kernel_regularizer=None,
+ bias_regularizer=None,
+ activity_regularizer=None,
+ kernel_constraint=None,
+ bias_constraint=None,
+ **kwargs):
+ """
+ Implements an AttentionDecoder that takes in a sequence encoded by an
+ encoder and outputs the decoded states
+ :param units: dimension of the hidden state and the attention matrices
+ :param output_dim: the number of labels in the output space
+
+ references:
+ Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio.
+ "Neural machine translation by jointly learning to align and translate."
+ arXiv preprint arXiv:1409.0473 (2014).
+ """
+ self.units = units
+ self.output_dim = output_dim
+ self.return_probabilities = return_probabilities
+ self.activation = activations.get(activation)
+ self.kernel_initializer = initializers.get(kernel_initializer)
+ self.recurrent_initializer = initializers.get(recurrent_initializer)
+ self.bias_initializer = initializers.get(bias_initializer)
+
+ self.kernel_regularizer = regularizers.get(kernel_regularizer)
+ self.recurrent_regularizer = regularizers.get(kernel_regularizer)
+ self.bias_regularizer = regularizers.get(bias_regularizer)
+ self.activity_regularizer = regularizers.get(activity_regularizer)
+
+ self.kernel_constraint = constraints.get(kernel_constraint)
+ self.recurrent_constraint = constraints.get(kernel_constraint)
+ self.bias_constraint = constraints.get(bias_constraint)
+
+ super(AttentionDecoder, self).__init__(**kwargs)
+ self.name = name
+ self.return_sequences = True # must return sequences
+
+ def build(self, input_shape):
+ """
+ See Appendix 2 of Bahdanau 2014, arXiv:1409.0473
+ for model details that correspond to the matrices here.
+ """
+
+ self.batch_size, self.timesteps, self.input_dim = input_shape
+
+ if self.stateful:
+ super(AttentionDecoder, self).reset_states()
+
+ self.states = [None, None] # y, s
+
+ """
+ Matrices for creating the context vector
+ """
+
+ self.V_a = self.add_weight(shape=(self.units,),
+ name='V_a',
+ initializer=self.kernel_initializer,
+ regularizer=self.kernel_regularizer,
+ constraint=self.kernel_constraint)
+ self.W_a = self.add_weight(shape=(self.units, self.units),
+ name='W_a',
+ initializer=self.kernel_initializer,
+ regularizer=self.kernel_regularizer,
+ constraint=self.kernel_constraint)
+ self.U_a = self.add_weight(shape=(self.input_dim, self.units),
+ name='U_a',
+ initializer=self.kernel_initializer,
+ regularizer=self.kernel_regularizer,
+ constraint=self.kernel_constraint)
+ self.b_a = self.add_weight(shape=(self.units,),
+ name='b_a',
+ initializer=self.bias_initializer,
+ regularizer=self.bias_regularizer,
+ constraint=self.bias_constraint)
+ """
+ Matrices for the r (reset) gate
+ """
+ self.C_r = self.add_weight(shape=(self.input_dim, self.units),
+ name='C_r',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.U_r = self.add_weight(shape=(self.units, self.units),
+ name='U_r',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.W_r = self.add_weight(shape=(self.output_dim, self.units),
+ name='W_r',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.b_r = self.add_weight(shape=(self.units, ),
+ name='b_r',
+ initializer=self.bias_initializer,
+ regularizer=self.bias_regularizer,
+ constraint=self.bias_constraint)
+
+ """
+ Matrices for the z (update) gate
+ """
+ self.C_z = self.add_weight(shape=(self.input_dim, self.units),
+ name='C_z',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.U_z = self.add_weight(shape=(self.units, self.units),
+ name='U_z',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.W_z = self.add_weight(shape=(self.output_dim, self.units),
+ name='W_z',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.b_z = self.add_weight(shape=(self.units, ),
+ name='b_z',
+ initializer=self.bias_initializer,
+ regularizer=self.bias_regularizer,
+ constraint=self.bias_constraint)
+ """
+ Matrices for the proposal
+ """
+ self.C_p = self.add_weight(shape=(self.input_dim, self.units),
+ name='C_p',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.U_p = self.add_weight(shape=(self.units, self.units),
+ name='U_p',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.W_p = self.add_weight(shape=(self.output_dim, self.units),
+ name='W_p',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.b_p = self.add_weight(shape=(self.units, ),
+ name='b_p',
+ initializer=self.bias_initializer,
+ regularizer=self.bias_regularizer,
+ constraint=self.bias_constraint)
+ """
+ Matrices for making the final prediction vector
+ """
+ self.C_o = self.add_weight(shape=(self.input_dim, self.output_dim),
+ name='C_o',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.U_o = self.add_weight(shape=(self.units, self.output_dim),
+ name='U_o',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.W_o = self.add_weight(shape=(self.output_dim, self.output_dim),
+ name='W_o',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.b_o = self.add_weight(shape=(self.output_dim, ),
+ name='b_o',
+ initializer=self.bias_initializer,
+ regularizer=self.bias_regularizer,
+ constraint=self.bias_constraint)
+
+ # For creating the initial state:
+ self.W_s = self.add_weight(shape=(self.input_dim, self.units),
+ name='W_s',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+
+ self.input_spec = [
+ InputSpec(shape=(self.batch_size, self.timesteps, self.input_dim))]
+ self.built = True
+
+ def call(self, x):
+ # store the whole sequence so we can "attend" to it at each timestep
+ self.x_seq = x
+
+ # apply the a dense layer over the time dimension of the sequence
+ # do it here because it doesn't depend on any previous steps
+ # thefore we can save computation time:
+ self._uxpb = _time_distributed_dense(self.x_seq, self.U_a, b=self.b_a,
+ input_dim=self.input_dim,
+ timesteps=self.timesteps,
+ output_dim=self.units)
+
+ return super(AttentionDecoder, self).call(x)
+
+ def get_initial_state(self, inputs):
+ print('inputs shape:', inputs.get_shape())
+
+ # apply the matrix on the first time step to get the initial s0.
+ s0 = activations.tanh(K.dot(inputs[:, 0], self.W_s))
+
+ # from keras.layers.recurrent to initialize a vector of (batchsize,
+ # output_dim)
+ y0 = K.zeros_like(inputs) # (samples, timesteps, input_dims)
+ y0 = K.sum(y0, axis=(1, 2)) # (samples, )
+ y0 = K.expand_dims(y0) # (samples, 1)
+ y0 = K.tile(y0, [1, self.output_dim])
+
+ return [y0, s0]
+
+ def step(self, x, states):
+
+ ytm, stm = states
+
+ # repeat the hidden state to the length of the sequence
+ _stm = K.repeat(stm, self.timesteps)
+
+ # now multiplty the weight matrix with the repeated hidden state
+ _Wxstm = K.dot(_stm, self.W_a)
+
+ # calculate the attention probabilities
+ # this relates how much other timesteps contributed to this one.
+ et = K.dot(activations.tanh(_Wxstm + self._uxpb),
+ K.expand_dims(self.V_a))
+ at = K.exp(et)
+ at_sum = K.sum(at, axis=1)
+ at_sum_repeated = K.repeat(at_sum, self.timesteps)
+ at /= at_sum_repeated # vector of size (batchsize, timesteps, 1)
+
+ # calculate the context vector
+ context = K.squeeze(K.batch_dot(at, self.x_seq, axes=1), axis=1)
+ # ~~~> calculate new hidden state
+ # first calculate the "r" gate:
+
+ rt = activations.sigmoid(
+ K.dot(ytm, self.W_r)
+ + K.dot(stm, self.U_r)
+ + K.dot(context, self.C_r)
+ + self.b_r)
+
+ # now calculate the "z" gate
+ zt = activations.sigmoid(
+ K.dot(ytm, self.W_z)
+ + K.dot(stm, self.U_z)
+ + K.dot(context, self.C_z)
+ + self.b_z)
+
+ # calculate the proposal hidden state:
+ s_tp = activations.tanh(
+ K.dot(ytm, self.W_p)
+ + K.dot((rt * stm), self.U_p)
+ + K.dot(context, self.C_p)
+ + self.b_p)
+
+ # new hidden state:
+ st = (1-zt)*stm + zt * s_tp
+
+ yt = activations.softmax(
+ K.dot(ytm, self.W_o)
+ + K.dot(stm, self.U_o)
+ + K.dot(context, self.C_o)
+ + self.b_o)
+
+ if self.return_probabilities:
+ return at, [yt, st]
+ else:
+ return yt, [yt, st]
+
+ def compute_output_shape(self, input_shape):
+ """
+ For Keras internal compatability checking
+ """
+ if self.return_probabilities:
+ return (None, self.timesteps, self.timesteps)
+ else:
+ return (None, self.timesteps, self.output_dim)
+
+ def get_config(self):
+ """
+ For rebuilding models on load time.
+ """
+ config = {
+ 'output_dim': self.output_dim,
+ 'units': self.units,
+ 'return_probabilities': self.return_probabilities
+ }
+ base_config = super(AttentionDecoder, self).get_config()
+ return dict(list(base_config.items()) + list(config.items()))
\ No newline at end of file
diff --git a/code_jurica/attentionDecoder.py b/code_jurica/attentionDecoder.py
new file mode 100644
index 0000000..863ef52
--- /dev/null
+++ b/code_jurica/attentionDecoder.py
@@ -0,0 +1,361 @@
+import tensorflow as tf
+from keras import backend as K
+from keras import regularizers, constraints, initializers, activations
+from keras.layers.recurrent import Recurrent
+from keras.engine import InputSpec
+
+import keras.backend as K
+
+def _time_distributed_dense(x, w, b=None, dropout=None,
+ input_dim=None, output_dim=None,
+ timesteps=None, training=None):
+ """Apply `y . w + b` for every temporal slice y of x.
+ # Arguments
+ x: input tensor.
+ w: weight matrix.
+ b: optional bias vector.
+ dropout: wether to apply dropout (same dropout mask
+ for every temporal slice of the input).
+ input_dim: integer; optional dimensionality of the input.
+ output_dim: integer; optional dimensionality of the output.
+ timesteps: integer; optional number of timesteps.
+ training: training phase tensor or boolean.
+ # Returns
+ Output tensor.
+ """
+ if not input_dim:
+ input_dim = K.shape(x)[2]
+ if not timesteps:
+ timesteps = K.shape(x)[1]
+ if not output_dim:
+ output_dim = K.shape(w)[1]
+
+ if dropout is not None and 0. < dropout < 1.:
+ # apply the same dropout pattern at every timestep
+ ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim)))
+ dropout_matrix = K.dropout(ones, dropout)
+ expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps)
+ x = K.in_train_phase(x * expanded_dropout_matrix, x, training=training)
+
+ # collapse time dimension and batch dimension together
+ x = K.reshape(x, (-1, input_dim))
+ x = K.dot(x, w)
+ if b is not None:
+ x = K.bias_add(x, b)
+ # reshape to 3D tensor
+ if K.backend() == 'tensorflow':
+ x = K.reshape(x, K.stack([-1, timesteps, output_dim]))
+ x.set_shape([None, None, output_dim])
+ else:
+ x = K.reshape(x, (-1, timesteps, output_dim))
+ return x
+
+tfPrint = lambda d, T: tf.Print(input_=T, data=[T, tf.shape(T)], message=d)
+
+class AttentionDecoder(Recurrent):
+
+ def __init__(self, units, output_dim,
+ activation='tanh',
+ return_probabilities=False,
+ name='AttentionDecoder',
+ kernel_initializer='glorot_uniform',
+ recurrent_initializer='orthogonal',
+ bias_initializer='zeros',
+ kernel_regularizer=None,
+ bias_regularizer=None,
+ activity_regularizer=None,
+ kernel_constraint=None,
+ bias_constraint=None,
+ **kwargs):
+ """
+ Implements an AttentionDecoder that takes in a sequence encoded by an
+ encoder and outputs the decoded states
+ :param units: dimension of the hidden state and the attention matrices
+ :param output_dim: the number of labels in the output space
+
+ references:
+ Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio.
+ "Neural machine translation by jointly learning to align and translate."
+ arXiv preprint arXiv:1409.0473 (2014).
+ """
+ self.units = units
+ self.output_dim = output_dim
+ self.return_probabilities = return_probabilities
+ self.activation = activations.get(activation)
+ self.kernel_initializer = initializers.get(kernel_initializer)
+ self.recurrent_initializer = initializers.get(recurrent_initializer)
+ self.bias_initializer = initializers.get(bias_initializer)
+
+ self.kernel_regularizer = regularizers.get(kernel_regularizer)
+ self.recurrent_regularizer = regularizers.get(kernel_regularizer)
+ self.bias_regularizer = regularizers.get(bias_regularizer)
+ self.activity_regularizer = regularizers.get(activity_regularizer)
+
+ self.kernel_constraint = constraints.get(kernel_constraint)
+ self.recurrent_constraint = constraints.get(kernel_constraint)
+ self.bias_constraint = constraints.get(bias_constraint)
+
+ super(AttentionDecoder, self).__init__(**kwargs)
+ self.name = name
+ self.return_sequences = True # must return sequences
+
+ def build(self, input_shape):
+ """
+ See Appendix 2 of Bahdanau 2014, arXiv:1409.0473
+ for model details that correspond to the matrices here.
+ """
+
+ self.batch_size, self.timesteps, self.input_dim = input_shape
+
+ if self.stateful:
+ super(AttentionDecoder, self).reset_states()
+
+ self.states = [None, None] # y, s
+
+ """
+ Matrices for creating the context vector
+ """
+
+ self.V_a = self.add_weight(shape=(self.units,),
+ name='V_a',
+ initializer=self.kernel_initializer,
+ regularizer=self.kernel_regularizer,
+ constraint=self.kernel_constraint)
+ self.W_a = self.add_weight(shape=(self.units, self.units),
+ name='W_a',
+ initializer=self.kernel_initializer,
+ regularizer=self.kernel_regularizer,
+ constraint=self.kernel_constraint)
+ self.U_a = self.add_weight(shape=(self.input_dim, self.units),
+ name='U_a',
+ initializer=self.kernel_initializer,
+ regularizer=self.kernel_regularizer,
+ constraint=self.kernel_constraint)
+ self.b_a = self.add_weight(shape=(self.units,),
+ name='b_a',
+ initializer=self.bias_initializer,
+ regularizer=self.bias_regularizer,
+ constraint=self.bias_constraint)
+ """
+ Matrices for the r (reset) gate
+ """
+ self.C_r = self.add_weight(shape=(self.input_dim, self.units),
+ name='C_r',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.U_r = self.add_weight(shape=(self.units, self.units),
+ name='U_r',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.W_r = self.add_weight(shape=(self.output_dim, self.units),
+ name='W_r',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.b_r = self.add_weight(shape=(self.units, ),
+ name='b_r',
+ initializer=self.bias_initializer,
+ regularizer=self.bias_regularizer,
+ constraint=self.bias_constraint)
+
+ """
+ Matrices for the z (update) gate
+ """
+ self.C_z = self.add_weight(shape=(self.input_dim, self.units),
+ name='C_z',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.U_z = self.add_weight(shape=(self.units, self.units),
+ name='U_z',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.W_z = self.add_weight(shape=(self.output_dim, self.units),
+ name='W_z',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.b_z = self.add_weight(shape=(self.units, ),
+ name='b_z',
+ initializer=self.bias_initializer,
+ regularizer=self.bias_regularizer,
+ constraint=self.bias_constraint)
+ """
+ Matrices for the proposal
+ """
+ self.C_p = self.add_weight(shape=(self.input_dim, self.units),
+ name='C_p',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.U_p = self.add_weight(shape=(self.units, self.units),
+ name='U_p',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.W_p = self.add_weight(shape=(self.output_dim, self.units),
+ name='W_p',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.b_p = self.add_weight(shape=(self.units, ),
+ name='b_p',
+ initializer=self.bias_initializer,
+ regularizer=self.bias_regularizer,
+ constraint=self.bias_constraint)
+ """
+ Matrices for making the final prediction vector
+ """
+ self.C_o = self.add_weight(shape=(self.input_dim, self.output_dim),
+ name='C_o',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.U_o = self.add_weight(shape=(self.units, self.output_dim),
+ name='U_o',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.W_o = self.add_weight(shape=(self.output_dim, self.output_dim),
+ name='W_o',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+ self.b_o = self.add_weight(shape=(self.output_dim, ),
+ name='b_o',
+ initializer=self.bias_initializer,
+ regularizer=self.bias_regularizer,
+ constraint=self.bias_constraint)
+
+ # For creating the initial state:
+ self.W_s = self.add_weight(shape=(self.input_dim, self.units),
+ name='W_s',
+ initializer=self.recurrent_initializer,
+ regularizer=self.recurrent_regularizer,
+ constraint=self.recurrent_constraint)
+
+ self.input_spec = [
+ InputSpec(shape=(self.batch_size, self.timesteps, self.input_dim))]
+ self.built = True
+
+ def call(self, x):
+ # store the whole sequence so we can "attend" to it at each timestep
+ self.x_seq = x
+
+ # apply the a dense layer over the time dimension of the sequence
+ # do it here because it doesn't depend on any previous steps
+ # thefore we can save computation time:
+ self._uxpb = _time_distributed_dense(self.x_seq, self.U_a, b=self.b_a,
+ input_dim=self.input_dim,
+ timesteps=self.timesteps,
+ output_dim=self.units)
+
+ return super(AttentionDecoder, self).call(x)
+
+ def get_initial_state(self, inputs):
+ print('inputs shape:', inputs.get_shape())
+
+ # apply the matrix on the first time step to get the initial s0.
+ s0 = activations.tanh(K.dot(inputs[:, 0], self.W_s))
+
+ # from keras.layers.recurrent to initialize a vector of (batchsize,
+ # output_dim)
+ y0 = K.zeros_like(inputs) # (samples, timesteps, input_dims)
+ y0 = K.sum(y0, axis=(1, 2)) # (samples, )
+ y0 = K.expand_dims(y0) # (samples, 1)
+ y0 = K.tile(y0, [1, self.output_dim])
+
+ return [y0, s0]
+
+ def step(self, x, states):
+
+ ytm, stm = states
+
+ # repeat the hidden state to the length of the sequence
+ _stm = K.repeat(stm, self.timesteps)
+
+ # now multiplty the weight matrix with the repeated hidden state
+ _Wxstm = K.dot(_stm, self.W_a)
+
+ # calculate the attention probabilities
+ # this relates how much other timesteps contributed to this one.
+ et = K.dot(activations.tanh(_Wxstm + self._uxpb),
+ K.expand_dims(self.V_a))
+ at = K.exp(et)
+ at_sum = K.sum(at, axis=1)
+ at_sum_repeated = K.repeat(at_sum, self.timesteps)
+ at /= at_sum_repeated # vector of size (batchsize, timesteps, 1)
+
+ # calculate the context vector
+ context = K.squeeze(K.batch_dot(at, self.x_seq, axes=1), axis=1)
+ # ~~~> calculate new hidden state
+ # first calculate the "r" gate:
+
+ rt = activations.sigmoid(
+ K.dot(ytm, self.W_r)
+ + K.dot(stm, self.U_r)
+ + K.dot(context, self.C_r)
+ + self.b_r)
+
+ # now calculate the "z" gate
+ zt = activations.sigmoid(
+ K.dot(ytm, self.W_z)
+ + K.dot(stm, self.U_z)
+ + K.dot(context, self.C_z)
+ + self.b_z)
+
+ # calculate the proposal hidden state:
+ s_tp = activations.tanh(
+ K.dot(ytm, self.W_p)
+ + K.dot((rt * stm), self.U_p)
+ + K.dot(context, self.C_p)
+ + self.b_p)
+
+ # new hidden state:
+ st = (1-zt)*stm + zt * s_tp
+
+ yt = activations.softmax(
+ K.dot(ytm, self.W_o)
+ + K.dot(stm, self.U_o)
+ + K.dot(context, self.C_o)
+ + self.b_o)
+
+ if self.return_probabilities:
+ return at, [yt, st]
+ else:
+ return yt, [yt, st]
+
+ def compute_output_shape(self, input_shape):
+ """
+ For Keras internal compatability checking
+ """
+ if self.return_probabilities:
+ return (None, self.timesteps, self.timesteps)
+ else:
+ return (None, self.timesteps, self.output_dim)
+
+ def get_config(self):
+ """
+ For rebuilding models on load time.
+ """
+ config = {
+ 'output_dim': self.output_dim,
+ 'units': self.units,
+ 'return_probabilities': self.return_probabilities
+ }
+ base_config = super(AttentionDecoder, self).get_config()
+ return dict(list(base_config.items()) + list(config.items()))
+
+# check to see if it compiles
+if __name__ == '__main__':
+ from keras.layers import Input, LSTM
+ from keras.models import Model
+ from keras.layers.wrappers import Bidirectional
+ i = Input(shape=(100,104), dtype='float32')
+ enc = Bidirectional(LSTM(64, return_sequences=True), merge_mode='concat')(i)
+ dec = AttentionDecoder(32, 4)(enc)
+ model = Model(inputs=i, outputs=dec)
+ model.summary()
\ No newline at end of file
diff --git a/code_jurica/loader.py b/code_jurica/loader.py
index d8f8076..406144f 100644
--- a/code_jurica/loader.py
+++ b/code_jurica/loader.py
@@ -95,7 +95,8 @@ source_embedding_layer = Embedding(source_embeddings.shape[0],
weights=[source_embeddings],
input_length=source_max_sequence_tokenizer,
trainable=True,
- mask_zero=True)
+ mask_zero=True,
+ name='source_embedding')
target_embeddings=embedding_matrix(target_vocab)
target_embedding_layer = Embedding(target_embeddings.shape[0],
@@ -103,7 +104,8 @@ target_embedding_layer = Embedding(target_embeddings.shape[0],
weights=[target_embeddings],
input_length=target_max_sequence_tokenizer,
trainable=True,
- mask_zero=True)
+ mask_zero=True,
+ name='target_embedding')
#generate train/test split
source_train, source_val, _, _ = train_test_split(source_corpus, labels, test_size=0.05, random_state=777)
diff --git a/code_jurica/logs/classification_report_test.csv b/code_jurica/logs/classification_report_test.csv
index 85f2b97..70e8143 100644
--- a/code_jurica/logs/classification_report_test.csv
+++ b/code_jurica/logs/classification_report_test.csv
@@ -1,966 +1,708 @@
Classes,precision,recall,f1-score,support
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-avg/total,0.7,0.58,0.6,7474
+Z948,1.0,1.0,1.0,1
+Z988,0.0,0.0,0.0,1
+Z991,0.0,0.0,0.0,0
+Z998,0.0,0.0,0.0,1
+avg/total,0.73,0.61,0.61,4505
diff --git a/code_jurica/seq2seq_attention.py b/code_jurica/seq2seq_attention.py
index d3688ed..9d17d2f 100644
--- a/code_jurica/seq2seq_attention.py
+++ b/code_jurica/seq2seq_attention.py
@@ -2,7 +2,7 @@
# experiment = Experiment(api_key="hSd9vTj0EfMu72569YnVEvtvj")
from loader import *
-from _layers import AttentionWithContext, Attention
+from _layers import AttentionWithContext, Attention, AttentionDecoder
from keras.models import Model, load_model as keras_load_model
from keras.layers import Input, LSTM, Dense, Embedding, GRU, Activation, dot, concatenate, Bidirectional, TimeDistributed
from keras.utils import multi_gpu_model
@@ -92,26 +92,26 @@ validation_data_generator = KerasBatchGenerator(batch_size,
print("Lets train some stuff!")
# Define an input sequence and process it.
-encoder_input = Input(shape=(source_max_sequence_tokenizer, ))
-x = source_embedding_layer(encoder_input)
-encoder_out, state_h, state_c = LSTM(latent_dim, return_sequences=True, unroll=True, return_state=True)(x)
+encoder_input = Input(shape=(source_max_sequence_tokenizer, ), name='encoder_input')
+x_encoder = source_embedding_layer(encoder_input)
+encoder_out, state_h, state_c = LSTM(latent_dim, return_sequences=True, unroll=True, return_state=True, name='encoder_lstm')(x_encoder)
encoder_states = [state_h, state_c]
# Set up the decoder, using `encoder_states` as initial state.
-decoder_input = Input(shape=(target_max_sequence_tokenizer, ))
+decoder_input = Input(shape=(target_max_sequence_tokenizer, ), name='decoder_input')
x_decode = target_embedding_layer(decoder_input)
-decoder_LSTM = LSTM(latent_dim, return_sequences=True, return_state = True, unroll=True)
+decoder_LSTM = LSTM(latent_dim, return_sequences=True, return_state = True, unroll=True, name='decoder_lstm')
decoder, state_h_decode , state_c_decode = decoder_LSTM(x_decode, initial_state=encoder_states)
# Equation (7) with 'dot' score from Section 3.1 in the paper.
# Note that we reuse Softmax-activation layer instead of writing tensor calculation
-attention = dot([encoder_out, decoder], axes=[2, 2])
-attention = Activation('softmax')(attention)
-context = dot([attention, encoder_out], axes=[1,1])
+attention = dot([encoder_out, decoder], name='attention_dot' ,axes=[2, 2])
+attention = Activation('softmax', name='attention_activation')(attention)
+context = dot([attention, encoder_out], name='context_dot' ,axes=[1,1])
decoder_combined_context = concatenate([context, decoder])
print(decoder_combined_context)
-decoder_dense = Dense(len(target_vocab)+1, activation='softmax')
+decoder_dense = Dense(len(target_vocab)+1, activation='softmax', name='dense_output')
decoder_out = decoder_dense(decoder_combined_context) # equation (6) of the paper
# MODEL
@@ -141,10 +141,11 @@ model.fit_generator(
# INFERENCE MODELS
# Encoder inference model
encoder_model_inf = Model(encoder_input, encoder_states)
+encoder_model_inf.summary()
# Decoder inference model
-decoder_state_input_h = Input(shape=(256,))
-decoder_state_input_c = Input(shape=(256,))
+decoder_state_input_h = Input(shape=(256,), name='inference_decoder_input_h')
+decoder_state_input_c = Input(shape=(256,), name='inference_decoder_input_c')
decoder_input_states = [decoder_state_input_h, decoder_state_input_c]
decoder, decoder_h, decoder_c = decoder_LSTM(x_decode, initial_state=decoder_input_states)
@@ -154,13 +155,14 @@ attention = dot([encoder_out, decoder], axes=[2, 2])
attention = Activation('softmax')(attention)
context = dot([attention, encoder_out], axes=[1,1])
-print(context, decoder)
+# print(context, decoder)
decoder_combined_context = concatenate([context, decoder])
-print('decoder_combined_context\t', decoder_combined_context)
+# print('decoder_combined_context\t', decoder_combined_context)
decoder_out = decoder_dense(decoder_combined_context)
decoder_model_inf = Model(inputs=[decoder_input] + decoder_input_states,
outputs=[decoder_out] + decoder_states )
+decoder_model_inf.summary()
def decode_seq(inp_seq):
diff --git a/code_jurica/train.sh b/code_jurica/train.sh
index 0165144..cae9235 100644
--- a/code_jurica/train.sh
+++ b/code_jurica/train.sh
@@ -1,4 +1,4 @@
#!/bin/bash
-#CUDA_VISIBLE_DEVICES=3 /home/sevajuri/anaconda3/bin/python3 /home/sevajuri/projects/clef18/code_jurica/classificationICD10_attention.py
+CUDA_VISIBLE_DEVICES=3 /home/sevajuri/anaconda3/bin/python3 /home/sevajuri/projects/clef18/code_jurica/classificationICD10_attention.py
CUDA_VISIBLE_DEVICES=3 /home/sevajuri/anaconda3/bin/python3 /home/sevajuri/projects/clef18/code_jurica/seq2seq.py
CUDA_VISIBLE_DEVICES=3 /home/sevajuri/anaconda3/bin/python3 /home/sevajuri/projects/clef18/code_jurica/test.py
--
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