add new demo.py

Author: 李闯

chatbotv4/demo.py

@@ -0,0 +1,161 @@
+# coding:utf-8
+import sys
+import numpy as np
+import tensorflow as tf
+from tensorflow.contrib.legacy_seq2seq.python.ops import seq2seq
+
+# 输入序列长度
+input_seq_len = 5
+# 输出序列长度
+output_seq_len = 5
+# 空值填充0
+PAD_ID = 0
+# 输出序列起始标记
+GO_ID = 1
+# 结尾标记
+EOS_ID = 2
+# LSTM神经元size
+size = 8
+# 最大输入符号数
+num_encoder_symbols = 10
+# 最大输出符号数
+num_decoder_symbols = 16
+# 学习率
+learning_rate = 0.1
+
+
+def get_samples():
+    """构造样本数据
+
+    :return:
+        encoder_inputs: [array([0, 0], dtype=int32), array([0, 0], dtype=int32), array([1, 3], dtype=int32),
+                        array([3, 5], dtype=int32), array([5, 7], dtype=int32)]
+        decoder_inputs: [array([1, 1], dtype=int32), array([7, 9], dtype=int32), array([ 9, 11], dtype=int32),
+                        array([11, 13], dtype=int32), array([0, 0], dtype=int32)]
+    """
+    train_set = [[[5, 7, 9], [11, 13, 15, EOS_ID]], [[5, 7, 9], [11, 13, 15, EOS_ID]]]
+    encoder_input_0 = [PAD_ID] * (input_seq_len - len(train_set[0][0])) + train_set[0][0]
+    encoder_input_1 = [PAD_ID] * (input_seq_len - len(train_set[1][0])) + train_set[1][0]
+    decoder_input_0 = [GO_ID] + train_set[0][1] + [PAD_ID] * (output_seq_len - len(train_set[0][1]) - 1)
+    decoder_input_1 = [GO_ID] + train_set[1][1] + [PAD_ID] * (output_seq_len - len(train_set[1][1]) - 1)
+
+    encoder_inputs = []
+    decoder_inputs = []
+    target_weights = []
+    for length_idx in xrange(input_seq_len):
+        encoder_inputs.append(np.array([encoder_input_0[length_idx], encoder_input_1[length_idx]], dtype=np.int32))
+    for length_idx in xrange(output_seq_len):
+        decoder_inputs.append(np.array([decoder_input_0[length_idx], decoder_input_1[length_idx]], dtype=np.int32))
+        target_weights.append(np.array([
+            0.0 if length_idx == output_seq_len - 1 or decoder_input_0[length_idx] == PAD_ID else 1.0,
+            0.0 if length_idx == output_seq_len - 1 or decoder_input_1[length_idx] == PAD_ID else 1.0,
+        ], dtype=np.float32))
+    return encoder_inputs, decoder_inputs, target_weights
+
+
+def get_model(feed_previous=False):
+    """构造模型
+    """
+    encoder_inputs = []
+    decoder_inputs = []
+    target_weights = []
+    for i in xrange(input_seq_len):
+        encoder_inputs.append(tf.placeholder(tf.int32, shape=[None], name="encoder{0}".format(i)))
+    for i in xrange(output_seq_len + 1):
+        decoder_inputs.append(tf.placeholder(tf.int32, shape=[None], name="decoder{0}".format(i)))
+    for i in xrange(output_seq_len):
+        target_weights.append(tf.placeholder(tf.float32, shape=[None], name="weight{0}".format(i)))
+
+    # decoder_inputs左移一个时序作为targets
+    targets = [decoder_inputs[i + 1] for i in xrange(output_seq_len)]
+
+    cell = tf.contrib.rnn.BasicLSTMCell(size)
+
+    # 这里输出的状态我们不需要
+    outputs, _ = seq2seq.embedding_attention_seq2seq(
+                        encoder_inputs,
+                        decoder_inputs[:output_seq_len],
+                        cell,
+                        num_encoder_symbols=num_encoder_symbols,
+                        num_decoder_symbols=num_decoder_symbols,
+                        embedding_size=size,
+                        output_projection=None,
+                        feed_previous=feed_previous,
+                        dtype=tf.float32)
+
+    # 计算加权交叉熵损失
+    loss = seq2seq.sequence_loss(outputs, targets, target_weights)
+    # 梯度下降优化器
+    opt = tf.train.GradientDescentOptimizer(learning_rate)
+    # 优化目标：让loss最小化
+    update = opt.apply_gradients(opt.compute_gradients(loss))
+    # 模型持久化
+    saver = tf.train.Saver(tf.global_variables())
+    return encoder_inputs, decoder_inputs, target_weights, outputs, loss, update, saver
+
+
+def train():
+    """
+    训练过程
+    """
+    with tf.Session() as sess:
+        sample_encoder_inputs, sample_decoder_inputs, sample_target_weights = get_samples()
+        encoder_inputs, decoder_inputs, target_weights, outputs, loss, update, saver = get_model()
+
+        input_feed = {}
+        for l in xrange(input_seq_len):
+            input_feed[encoder_inputs[l].name] = sample_encoder_inputs[l]
+        for l in xrange(output_seq_len):
+            input_feed[decoder_inputs[l].name] = sample_decoder_inputs[l]
+            input_feed[target_weights[l].name] = sample_target_weights[l]
+        input_feed[decoder_inputs[output_seq_len].name] = np.zeros([2], dtype=np.int32)
+
+        # 全部变量初始化
+        sess.run(tf.global_variables_initializer())
+
+        # 训练200次迭代，每隔10次打印一次loss
+        for step in xrange(200):
+            [loss_ret, _] = sess.run([loss, update], input_feed)
+            if step % 10 == 0:
+                print 'step=', step, 'loss=', loss_ret
+
+        # 模型持久化
+        saver.save(sess, './model/demo')
+
+
+def predict():
+    """
+    预测过程
+    """
+    with tf.Session() as sess:
+        sample_encoder_inputs, sample_decoder_inputs, sample_target_weights = get_samples()
+        encoder_inputs, decoder_inputs, target_weights, outputs, loss, update, saver = get_model(feed_previous=True)
+        # 从文件恢复模型
+        saver.restore(sess, './model/demo')
+
+        input_feed = {}
+        for l in xrange(input_seq_len):
+            input_feed[encoder_inputs[l].name] = sample_encoder_inputs[l]
+        for l in xrange(output_seq_len):
+            input_feed[decoder_inputs[l].name] = sample_decoder_inputs[l]
+            input_feed[target_weights[l].name] = sample_target_weights[l]
+        input_feed[decoder_inputs[output_seq_len].name] = np.zeros([2], dtype=np.int32)
+
+        # 预测输出
+        outputs = sess.run(outputs, input_feed)
+        # 一共试验样本有2个，所以分别遍历
+        for sample_index in xrange(2):
+            # 因为输出数据每一个是num_decoder_symbols维的，因此找到数值最大的那个就是预测的id，就是这里的argmax函数的功能
+            outputs_seq = [int(np.argmax(logit[sample_index], axis=0)) for logit in outputs]
+            # 如果是结尾符，那么后面的语句就不输出了
+            if EOS_ID in outputs_seq:
+                outputs_seq = outputs_seq[:outputs_seq.index(EOS_ID)]
+            outputs_seq = [str(v) for v in outputs_seq]
+            print " ".join(outputs_seq)
+
+
+if __name__ == "__main__":
+    if sys.argv[1] == 'train':
+        train()
+    else:
+        predict()