/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

0 vote341 views1 answer
Module level import not at top of file
Python
0 vote359 views1 answer
Numpy empty image
Python
0 vote341 views1 answer
First line of input contains integer N (size of the array) Second line contains N space seperated integers (elements of the array) Third line contains two integers L and R. (L<=R)
Python
0 vote350 views1 answer
Train test validation sklearn
Python
0 vote351 views1 answer
Islink(node1 node2) is used for
Python
0 vote331 views1 answer
Counter +1 python
Python
28 votes327 views6 answers
Import JSON in python
Python
0 vote339 views1 answer
Calculate the R^2 for X and Y python
Python
1 vote327 views1 answer
Pandas .nlargest
Python
-1 vote309 views1 answer
Godot variablen einen wert hinzufügen
Python