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01.seq2seq_demo.py
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87 lines (68 loc) · 3.27 KB
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# -*- coding: utf-8 -*-
"""
@author:XuMing（[email protected])
@description:
"""
from seq2seq import SimpleSeq2Seq, Seq2Seq, AttentionSeq2Seq
import numpy as np
from keras.utils.test_utils import keras_test
input_length = 5
input_dim = 3
output_length = 3
output_dim = 4
samples = 100
hidden_dim = 24
@keras_test
def test_SimpleSeq2Seq():
x = np.random.random((samples, input_length, input_dim))
y = np.random.random((samples, output_length, output_dim))
models = []
print(x)
print(y)
models += [SimpleSeq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim))]
models += [SimpleSeq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), depth=2)]
for model in models:
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, nb_epoch=1)
@keras_test
def test_Seq2Seq():
x = np.random.random((samples, input_length, input_dim))
y = np.random.random((samples, output_length, output_dim))
models = []
models += [Seq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim))]
models += [Seq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), peek=True)]
models += [Seq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), depth=2)]
models += [Seq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), peek=True, depth=2)]
for model in models:
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1)
model = Seq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), peek=True, depth=2, teacher_force=True)
model.compile(loss='mse', optimizer='sgd')
model.fit([x, y], y, epochs=1)
@keras_test
def test_AttentionSeq2Seq():
x = np.random.random((samples, input_length, input_dim))
y = np.random.random((samples, output_length, output_dim))
models = []
models += [AttentionSeq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim))]
models += [AttentionSeq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), depth=2)]
models += [AttentionSeq2Seq(output_dim=output_dim, hidden_dim=hidden_dim, output_length=output_length,
input_shape=(input_length, input_dim), depth=3)]
for model in models:
model.compile(loss='mse', optimizer='sgd')
model.fit(x, y, epochs=1)
# test_SimpleSeq2Seq()
# test_Seq2Seq()
# test_AttentionSeq2Seq()
from seq2seq.models import AttentionSeq2Seq
model = AttentionSeq2Seq(input_dim=5, input_length=7, hidden_dim=10, output_length=8, output_dim=20, depth=4)
model.compile(loss='mse', optimizer='rmsprop')