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@zyxue zyxue/SRNN-vs-LSTM
Created Apr 17, 2018

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Train a single-neuron RNN to compare performance of vanilla RNN and LSTM on information latching
import matplotlib.pyplot as plt
import numpy as np
from keras.models import Model
from keras.layers import Input, LSTM, Dense, SimpleRNN
N = 10000
num_repeats = 30
num_epochs = 5
# sequence length options
lens = [2, 5, 8, 10, 15, 20, 25, 30] + np.arange(30, 210, 10).tolist()
res = {}
for (RNN_CELL, key) in zip([SimpleRNN, LSTM], ['srnn', 'lstm']):
res[key] = {}
print(key, end=': ')
for seq_len in lens:
print(seq_len, end=',')
xs = np.zeros((N, seq_len))
ys = np.zeros(N)
# construct input data
positive_indexes = np.arange(N // 2)
negative_indexes = np.arange(N // 2, N)
xs[positive_indexes, 0] = 1
ys[positive_indexes] = 1
xs[negative_indexes, 0] = -1
ys[negative_indexes] = 0
noise = np.random.normal(loc=0, scale=0.1, size=(N, seq_len))
train_xs = (xs + noise).reshape(N, seq_len, 1)
train_ys = ys
# repeat each experiments multiple times
hists = []
for i in range(num_repeats):
inputs = Input(shape=(None, 1), name='input')
rnn = RNN_CELL(1, input_shape=(None, 1), name='rnn')(inputs)
out = Dense(2, activation='softmax', name='output')(rnn)
model = Model(inputs, out)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
hist = model.fit(train_xs, train_ys, epochs=num_epochs, shuffle=True, validation_split=0.2, batch_size=16, verbose=0)
hists.append(hist.history['val_acc'][-1])
res[key][seq_len] = hists
print()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(pd.DataFrame.from_dict(res['lstm']).mean(), label='lstm')
ax.plot(pd.DataFrame.from_dict(res['srnn']).mean(), label='srnn')
ax.legend()
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