Recurrent Attention API: Cell wrapper base class

examples/recurrent_attention.py

@@ -0,0 +1,102 @@
+'''Canonical example of using attention for sequence to sequence problems.
+
+This script demonstrates how to use an RNNAttentionCell to implement attention
+mechanisms. In the example, the model only have to learn to filter the attended
+input to obtain the target. Basically it has to learn to "parse" the attended
+input sequence and output only relevant parts.
+
+# Explanation of data:
+
+One sample of input data consists of a sequence of one-hot-vectors separated
+by randomly added "extra" zero-vectors:
+
+    0 0 0 0 1 0 0 0 0 0
+    1 0 0 1 0 0 0 0 0 1
+    0 0 0 0 0 0 1 0 0 0
+    0 0 1 0 0 0 0 0 0 0
+    ^         ^
+    |         |
+    |         extra zero-vector
+    one-hot vector
+
+The goal is to retrieve the one-hot-vector sequence _without_ the extra zeros:
+
+    0 0 0 1 0 0
+    1 0 1 0 0 1
+    0 0 0 0 1 0
+    0 1 0 0 0 0
+
+# Summary of the algorithm
+
+The task is carried out by letting a Mixture Of Gaussian 1D attention mechanism
+attend to the input sequence (with the extra zeros) and select what information
+should be passed to the wrapped LSTM cell.
+
+# Attention vs. Encoder-Decoder approach
+This is good example where attention mechanisms are suitable. In this case
+attention clearly outperforms e.g. encoder-decoder approaches.
+TODO add this comparison to the script
+TODO add comparison heads=1 vs heads=2 (later converges faster)
+'''
+
+from __future__ import division, print_function
+
+import random
+
+import numpy as np
+
+from keras import Input
+from keras.engine import Model
+from keras.layers import Dense, TimeDistributed, LSTMCell, RNN
+
+from keras.layers.attention import MixtureOfGaussian1DAttention
+
+
+def get_training_data(n_samples,
+                      n_labels,
+                      n_timesteps_attended,
+                      n_timesteps_labels):
+    labels = np.random.randint(
+        n_labels,
+        size=(n_samples, n_timesteps_labels)
+    )
+    attended_time_idx = range(n_timesteps_attended)
+    label_time_idx = range(1, n_timesteps_labels + 1)
+
+    labels_one_hot = np.zeros((n_samples, n_timesteps_labels + 1, n_labels))
+    attended = np.zeros((n_samples, n_timesteps_attended, n_labels))
+    for i in range(n_samples):
+        labels_one_hot[i][label_time_idx, labels[i]] = 1
+        positions = sorted(random.sample(attended_time_idx, n_timesteps_labels))
+        attended[i][positions, labels[i]] = 1
+
+    return labels_one_hot, attended
+
+
+n_samples = 10000
+n_timesteps_labels = 10
+n_timesteps_attended = 30
+n_labels = 4
+
+input_labels = Input((n_timesteps_labels, n_labels))
+attended = Input((n_timesteps_attended, n_labels))
+
+cell = MixtureOfGaussian1DAttention(LSTMCell(64), components=3, heads=2)
+attention_lstm = RNN(cell, return_sequences=True)
+
+attention_lstm_output = attention_lstm(input_labels, constants=attended)
+output_layer = TimeDistributed(Dense(n_labels, activation='softmax'))
+output = output_layer(attention_lstm_output)
+
+model = Model(inputs=[input_labels, attended], outputs=output)
+
+labels_data, attended_data = get_training_data(n_samples,
+                                               n_labels,
+                                               n_timesteps_attended,
+                                               n_timesteps_labels)
+input_labels_data = labels_data[:, :-1, :]
+target_labels_data = labels_data[:, 1:, :]
+
+model.compile(optimizer='Adam', loss='categorical_crossentropy')
+model.fit(x=[input_labels_data, attended_data], y=target_labels_data, epochs=5)
+output_data = model.predict([input_labels_data, attended_data])

keras/activations.py

@@ -158,6 +158,10 @@ def linear(x):
     return x
 
 
+def exponential(x):
+    return K.exp(x)
+
+
 def serialize(activation):
     return activation.__name__