models.py

import numpy as np

import keras.backend as K
from keras.models import Sequential,Model
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers import Input
from keras.layers import Merge
from keras.utils import np_utils
from keras.layers import Conv1D, MaxPooling1D, Embedding
from keras.layers import Input, Embedding, Dense, merge


class CNNModel:
	def getModel(self, params_obj, weight=None  ):
		# Embeddings
		if weight==None or params_obj.use_pretrained_embeddings==False:
			weight=np.array(params_obj.vocab_size+1, params_obj.embeddings_dim).astype('float32')	
		embeddings_layer = Embedding(
			params_obj.vocab_size+1, # due to mask_zero
			params_obj.embeddings_dim,
			input_length=params_obj.inp_length,
			weights=[weight],
			trainable=params_obj.train_embedding
		)
		#Convolution
		inp = Input(shape=(params_obj.inp_length, params_obj.embeddings_dim))
		convolution_features_list = []
		for filter_size,pool_length,num_filters in zip(params_obj.filter_sizes, params_obj.filter_pool_lengths, params_obj.filter_sizes):
			conv_layer = Conv1D(nb_filter=num_filters, filter_length=filter_size, activation='relu')(inp)
			pool_layer = MaxPooling1D(pool_length=pool_length)(conv_layer)
			flatten = Flatten()(pool_layer)
			convolution_features_list.append(flatten)
		out = Merge(mode='concat')(convolution_features_list)
		network = Model(input=inp, output=out)
	
		# Model	
		model = Sequential()
		model.add(embeddings_layer)
		model.add(Dropout(params_obj.dropout_val, input_shape=(params_obj.vocab_size+1, params_obj.embeddings_dim)))
		model.add(network)

		#Add dense layer to complete the model
		model.add(Dense(params_obj.dense_layer_size,init='uniform',activation='relu'))
		model.add(Dropout(params_obj.dropout_val))
		model.add( Dense(params_obj.num_classes, init='uniform', activation='softmax')  )
		model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])	
		return model