ESIM using keras - Githubissues

Hi Since I don't have access to GPU, I can't execute your code, but there is another code in the github that implements your model with the keras Library . Are you confirming the following code and correct?

""" Implementation of ESIM(Enhanced LSTM for Natural Language Inference) https://arxiv.org/abs/1609.06038 """ import numpy as np from keras.layers import * from keras.activations import softmax from keras.models import Model

def StaticEmbedding(embedding_matrix): in_dim, out_dim = embedding_matrix.shape return Embedding(in_dim, out_dim, weights=[embedding_matrix], trainable=False)

def subtract(input_1, input_2): minus_input_2 = Lambda(lambda x: -x)(input_2) return add([input_1, minus_input_2])

def aggregate(input_1, input_2, num_dense=300, dropout_rate=0.5): feat1 = concatenate([GlobalAvgPool1D()(input_1), GlobalMaxPool1D()(input_1)]) feat2 = concatenate([GlobalAvgPool1D()(input_2), GlobalMaxPool1D()(input_2)]) x = concatenate([feat1, feat2]) x = BatchNormalization()(x) x = Dense(num_dense, activation='relu')(x) x = BatchNormalization()(x) x = Dropout(dropout_rate)(x) x = Dense(num_dense, activation='relu')(x) x = BatchNormalization()(x) x = Dropout(dropout_rate)(x) return x

def align(input_1, input_2): attention = Dot(axes=-1)([input_1, input_2]) w_att_1 = Lambda(lambda x: softmax(x, axis=1))(attention) w_att_2 = Permute((2,1))(Lambda(lambda x: softmax(x, axis=2))(attention)) in1_aligned = Dot(axes=1)([w_att_1, input_1]) in2_aligned = Dot(axes=1)([w_att_2, input_2]) return in1_aligned, in2_aligned

def build_model(embedding_matrix, num_class=1, max_length=30, lstm_dim=300): q1 = Input(shape=(max_length,)) q2 = Input(shape=(max_length,))

# Embedding
embedding = StaticEmbedding(embedding_matrix)
q1_embed = BatchNormalization(axis=2)(embedding(q1))
q2_embed = BatchNormalization(axis=2)(embedding(q2))

# Encoding
encode = Bidirectional(LSTM(lstm_dim, return_sequences=True))
q1_encoded = encode(q1_embed)
q2_encoded = encode(q2_embed)

# Alignment
q1_aligned, q2_aligned = align(q1_encoded, q2_encoded)

# Compare
q1_combined = concatenate([q1_encoded, q2_aligned, subtract(q1_encoded, q2_aligned), multiply([q1_encoded, q2_aligned])])
q2_combined = concatenate([q2_encoded, q1_aligned, subtract(q2_encoded, q1_aligned), multiply([q2_encoded, q1_aligned])]) 
compare = Bidirectional(LSTM(lstm_dim, return_sequences=True))
q1_compare = compare(q1_combined)
q2_compare = compare(q2_combined)

# Aggregate
x = aggregate(q1_compare, q2_compare)
x = Dense(num_class, activation='sigmoid')(x)

return Model(inputs=[q1, q2], outputs=x)

link github: https://gist.github.com/namakemono/b74547e82ef9307da9c29057c650cdf1

lukecq1231 / nli

ESIM using keras #13