/Python/Keras functional api embedding layer

Keras functional api embedding layer

max_seq_length=100 #i.e., sentence has a max of 100 words 
word_weight_matrix = ... #this has a shape of 9825, 300, i.e., the vocabulary has 9825 words and each is a 300 dimension vector 
deep_inputs = Input(shape=(max_seq_length,))
embedding = Embedding(9826, 300, input_length=max_seq_length,
                      weights=[word_weight_matrix], trainable=False)(deep_inputs) # line A
hidden = Dense(targets, activation="softmax")(embedding)
model = Model(inputs=deep_inputs, outputs=hidden)

Source: stackoverflow.com

Similar codes

10 votes122 views4 answers
Python bold text
Python
0 vote126 views1 answer
Pandas remove item from dictionary
Python
10 votes133 views5 answers
Ordered dictionary python
Python
3 votes129 views3 answers
Qlabel click python
Python
0 vote133 views1 answer
Python get text of QLineEdit
Python
1 vote121 views1 answer
Sql alchemy engine all tables
Python
9 votes129 views4 answers
Json timestamp to date python
Python
0 vote127 views1 answer
Jaccard distance python
Python
5 votes315 views2 answers
Django connexion session time
Python Django
0 vote124 views1 answer
How to customize simplejwt error response message in django restframework
Python