129 lines
3.7 KiB
Python
129 lines
3.7 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import print_function
|
|
import numpy as np
|
|
from speech_data import speech_model_data
|
|
from keras.models import Model
|
|
from keras.layers import Input, Dense, Dropout, LSTM, Lambda
|
|
from keras.optimizers import RMSprop, SGD
|
|
from keras.callbacks import TensorBoard, ModelCheckpoint
|
|
from keras import backend as K
|
|
|
|
|
|
def euclidean_distance(vects):
|
|
x, y = vects
|
|
return K.sqrt(
|
|
K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
|
|
|
|
|
|
def eucl_dist_output_shape(shapes):
|
|
shape1, shape2 = shapes
|
|
return (shape1[0], 1)
|
|
|
|
|
|
def contrastive_loss(y_true, y_pred):
|
|
'''Contrastive loss from Hadsell-et-al.'06
|
|
http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
|
|
'''
|
|
return K.mean(y_true * K.square(y_pred) +
|
|
(1 - y_true) * K.square(K.maximum(1 - y_pred, 0)))
|
|
|
|
|
|
def create_base_rnn_network(input_dim):
|
|
'''Base network to be shared (eq. to feature extraction).
|
|
'''
|
|
inp = Input(shape=input_dim)
|
|
ls1 = LSTM(1024, return_sequences=True)(inp)
|
|
ls2 = LSTM(512, return_sequences=True)(ls1)
|
|
ls3 = LSTM(32)(ls2)
|
|
return Model(inp, ls3)
|
|
|
|
|
|
def create_base_network(input_dim):
|
|
'''Base network to be shared (eq. to feature extraction).
|
|
'''
|
|
input = Input(shape=input_dim)
|
|
x = Dense(128, activation='relu')(input)
|
|
x = Dropout(0.1)(x)
|
|
x = Dense(128, activation='relu')(x)
|
|
x = Dropout(0.1)(x)
|
|
x = Dense(128, activation='relu')(x)
|
|
return Model(input, x)
|
|
|
|
|
|
def compute_accuracy(y_true, y_pred):
|
|
'''Compute classification accuracy with a fixed threshold on distances.
|
|
'''
|
|
pred = y_pred.ravel() < 0.5
|
|
return np.mean(pred == y_true)
|
|
|
|
|
|
def accuracy(y_true, y_pred):
|
|
'''Compute classification accuracy with a fixed threshold on distances.
|
|
'''
|
|
return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))
|
|
|
|
|
|
# the data, shuffled and split between train and test sets
|
|
tr_pairs, te_pairs, tr_y, te_y = speech_model_data()
|
|
input_dim = (tr_pairs.shape[2], tr_pairs.shape[3])
|
|
|
|
# network definition
|
|
base_network = create_base_rnn_network(input_dim)
|
|
input_a = Input(shape=input_dim)
|
|
input_b = Input(shape=input_dim)
|
|
|
|
# because we re-use the same instance `base_network`,
|
|
# the weights of the network
|
|
# will be shared across the two branches
|
|
processed_a = base_network(input_a)
|
|
processed_b = base_network(input_b)
|
|
|
|
distance = Lambda(
|
|
euclidean_distance,
|
|
output_shape=eucl_dist_output_shape)([processed_a, processed_b])
|
|
|
|
model = Model([input_a, input_b], distance)
|
|
|
|
tb_cb = TensorBoard(
|
|
log_dir='./logs/siamese_logs',
|
|
histogram_freq=1,
|
|
batch_size=32,
|
|
write_graph=True,
|
|
write_grads=True,
|
|
write_images=True,
|
|
embeddings_freq=0,
|
|
embeddings_layer_names=None,
|
|
embeddings_metadata=None)
|
|
cp_file_fmt = './models/siamese_speech_model-{epoch:02d}-epoch-{val_acc:0.2f}\
|
|
-acc.h5'
|
|
|
|
cp_cb = ModelCheckpoint(
|
|
cp_file_fmt,
|
|
monitor='val_acc',
|
|
verbose=0,
|
|
save_best_only=False,
|
|
save_weights_only=False,
|
|
mode='auto',
|
|
period=1)
|
|
# train
|
|
rms = RMSprop(lr=0.001)
|
|
sgd = SGD(lr=0.001)
|
|
model.compile(loss=contrastive_loss, optimizer=rms, metrics=[accuracy])
|
|
model.fit(
|
|
[tr_pairs[:, 0], tr_pairs[:, 1]],
|
|
tr_y,
|
|
batch_size=128,
|
|
epochs=50,
|
|
validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),
|
|
callbacks=[tb_cb, cp_cb])
|
|
|
|
model.save('./models/siamese_speech_model-final.h5')
|
|
# compute final accuracy on training and test sets
|
|
y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
|
|
tr_acc = compute_accuracy(tr_y, y_pred)
|
|
y_pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])
|
|
te_acc = compute_accuracy(te_y, y_pred)
|
|
|
|
print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
|
|
print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))
|