vocab_size = len(vocab)
graph_in = Input((vocab_size, 100))
convs = [ ]
for fsz in range (3, 6):
x = Conv1D(64, fsz, padding='same', activation="relu")(graph_in)
x = MaxPooling1D()(x)
x = Flatten()(x)
convs.append(x)
out = Concatenate(axis=-1)(convs)
graph = Model(graph_in, out)
# In[27]:
embedding_layer.input_length = seq_len
# In[28]:
model = Sequential([
embedding_layer,
SpatialDropout1D(0.2),
Dropout (0.2),
graph,
Dropout (0.5),
Dense (100, activation="relu"),
Dropout (0.7),
Dense (1, activation='sigmoid')
])