Switch branches/tags
Nothing to show
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
74 lines (54 sloc) 3.42 KB
from keras.models import Model
from keras.optimizers import SGD, Adam
from keras.layers import Input, Dense, Dropout, Flatten, Lambda, Embedding
from keras.layers.convolutional import Convolution1D, MaxPooling1D
from keras.initializers import RandomNormal
def create_model(filter_kernels, dense_outputs, maxlen, vocab_size, nb_filter, cat_output):
initializer = RandomNormal(mean=0.0, stddev=0.05, seed=None)
# Define what the input shape looks like
inputs = Input(shape=(maxlen,), dtype='int64')
# Option one:
# Uncomment following code to use a lambda layer to create a onehot encoding of a sequence of characters on the fly.
# Holding one-hot encodings in memory is very inefficient.
# The output_shape of embedded layer will be: batch x maxlen x vocab_size
import tensorflow as tf
def one_hot(x):
return tf.one_hot(x, vocab_size, on_value=1.0, off_value=0.0, axis=-1, dtype=tf.float32)
def one_hot_outshape(in_shape):
return in_shape[0], in_shape[1], vocab_size
embedded = Lambda(one_hot, output_shape=one_hot_outshape)(inputs)
# Option two:
# Or, simply use Embedding layer as following instead of use lambda to create one-hot layer
# Think of it as a one-hot embedding and a linear layer mashed into a single layer.
# See discussion here:
# Note this will introduce one extra layer of weights (of size vocab_size x vocab_size = 69*69 = 4761)
# embedded = Embedding(input_dim=vocab_size, output_dim=vocab_size)(inputs)
# All the convolutional layers...
conv = Convolution1D(filters=nb_filter, kernel_size=filter_kernels[0], kernel_initializer=initializer,
padding='valid', activation='relu',
input_shape=(maxlen, vocab_size))(embedded)
conv = MaxPooling1D(pool_size=3)(conv)
conv1 = Convolution1D(filters=nb_filter, kernel_size=filter_kernels[1], kernel_initializer=initializer,
padding='valid', activation='relu')(conv)
conv1 = MaxPooling1D(pool_size=3)(conv1)
conv2 = Convolution1D(filters=nb_filter, kernel_size=filter_kernels[2], kernel_initializer=initializer,
padding='valid', activation='relu')(conv1)
conv3 = Convolution1D(filters=nb_filter, kernel_size=filter_kernels[3], kernel_initializer=initializer,
padding='valid', activation='relu')(conv2)
conv4 = Convolution1D(filters=nb_filter, kernel_size=filter_kernels[4], kernel_initializer=initializer,
padding='valid', activation='relu')(conv3)
conv5 = Convolution1D(filters=nb_filter, kernel_size=filter_kernels[5], kernel_initializer=initializer,
padding='valid', activation='relu')(conv4)
conv5 = MaxPooling1D(pool_size=3)(conv5)
conv5 = Flatten()(conv5)
# Two dense layers with dropout of .5
z = Dropout(0.5)(Dense(dense_outputs, activation='relu')(conv5))
z = Dropout(0.5)(Dense(dense_outputs, activation='relu')(z))
# Output dense layer with softmax activation
pred = Dense(cat_output, activation='softmax', name='output')(z)
model = Model(inputs=inputs, outputs=pred)
sgd = SGD(lr=0.01, momentum=0.9)
adam = Adam(lr=0.001) # Feel free to use SGD above. I found Adam with lr=0.001 is faster than SGD with lr=0.01
model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
return model