/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

4 votes215 views2 answers
Loginrequiredmixin
Python
0 vote211 views3 answers
303995
Python
2 votes222 views1 answer
Pytorch create tensor
Python
1 vote224 views1 answer
Get a list of open applications python
Python
0 vote187 views1 answer
Convert pandas group to dict
Python
21 votes194 views3 answers
How to create an empty list of certain length in python
Python
2 votes196 views2 answers
Area of trapezium
Python
0 vote273 views1 answer
Encanto meaning spanish
Python
2 votes190 views3 answers
Geopandas set crs
Python
0 vote209 views1 answer
Matplotlib point labels
Python