lib/exercises-in-programming-style
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SimpleRNN does the job equally well.

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Crista Lopes
2020-01-01 12:03:23 -08:00
parent 69e9934ae9
commit a6d5ee0d13
1 changed files with 2 additions and 2 deletions
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38-recurrent/tf-38.py
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@@ -1,5 +1,5 @@
from keras.models import Sequential from keras.models import Sequential
from keras.layers import Dense, LSTM from keras.layers import Dense, SimpleRNN
import numpy as np import numpy as np
import sys, os, string, random import sys, os, string, random
@@ -71,7 +71,7 @@ def train(model):
def build_model(): def build_model():
model = Sequential() model = Sequential()
model.add(LSTM(HIDDEN_SIZE, input_shape=(None, INPUT_VOCAB_SIZE))) model.add(SimpleRNN(HIDDEN_SIZE, input_shape=(None, INPUT_VOCAB_SIZE)))
model.add(Dense(INPUT_VOCAB_SIZE, activation='softmax')) model.add(Dense(INPUT_VOCAB_SIZE, activation='softmax'))
return model return model