編集履歴

質問編集履歴

ソースの追加

2018/11/19 08:52

投稿

hiro329

スコア19

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@@ -116,13 +116,13 @@
 ```
-```ここに言語を入力
+```ここに言語を入力
 from __future__ import absolute_import
@@ -194,12 +194,4 @@
     softmax_loss_function = None
-   ```
+```

ソースの追加

2018/11/19 08:52

投稿

hiro329

スコア19

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@@ -118,25 +118,9 @@
 ```ここに言語を入力
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-"""Sequence-to-sequence model with an attention mechanism."""
@@ -212,95 +196,7 @@
-    if num_samples > 0 and num_samples < self.target_vocab_size:
-      with tf.device("/cpu:0"):
-        w = tf.get_variable("proj_w", [size, self.target_vocab_size])
-        w_t = tf.transpose(w)
-        b = tf.get_variable("proj_b", [self.target_vocab_size])
-      output_projection = (w, b)
-      def sampled_loss(inputs, labels):
-        with tf.device("/cpu:0"):
-          labels = tf.reshape(labels, [-1, 1])
-          return tf.nn.sampled_softmax_loss(w_t, b, inputs, labels, num_samples,
-                                            self.target_vocab_size)
-      softmax_loss_function = sampled_loss
-    single_cell = rnn_cell.GRUCell(size)
-    if use_lstm:
-      single_cell = rnn_cell.BasicLSTMCell(size)
-    cell = single_cell
-    if num_layers > 1:
-      cell = rnn_cell.MultiRNNCell([single_cell] * num_layers)
-    def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
-      return seq2seq.embedding_attention_seq2seq(
-          encoder_inputs, decoder_inputs, cell, source_vocab_size,
-          target_vocab_size, output_projection=output_projection,
-          feed_previous=do_decode)
-    self.encoder_inputs = []
-    self.decoder_inputs = []
-    self.target_weights = []
-    for i in xrange(buckets[-1][0]):
-      self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
-                                                name="encoder{0}".format(i)))
-    for i in xrange(buckets[-1][1] + 1):
-      self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
-                                                name="decoder{0}".format(i)))
-      self.target_weights.append(tf.placeholder(tf.float32, shape=[None],
-                                                name="weight{0}".format(i)))
-    targets = [self.decoder_inputs[i + 1]
-               for i in xrange(len(self.decoder_inputs) - 1)]

ソースの追加

2018/11/19 08:50

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hiro329

スコア19

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@@ -21,3 +21,289 @@
 すべて、予測してくれるとは思ってはいないのですが、ここでエラーが出て進まないのですが、解決方法などを教えていただけると幸いです。
 よろしくお願いいたします。
+```ここに言語を入力
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import MeCab
+import math
+import os
+import random
+import sys
+import time
+import tensorflow.python.platform
+import numpy as np
+from six.moves import xrange
+import tensorflow as tf
+import data_utils
+from tensorflow.models.rnn.translate import seq2seq_model
+from tensorflow.python.platform import gfile
+tf.app.flags.DEFINE_float("learning_rate", 0.5, "Learning rate.")
+tf.app.flags.DEFINE_float("learning_rate_decay_factor", 0.99,
+                          "Learning rate decays by this much.")
+tf.app.flags.DEFINE_float("max_gradient_norm", 5.0,
+                          "Clip gradients to this norm.")
+tf.app.flags.DEFINE_integer("batch_size", 4,
+                            "Batch size to use during training.")
+tf.app.flags.DEFINE_integer("size", 256, "Size of each model layer.")
+tf.app.flags.DEFINE_integer("num_layers", 2, "Number of layers in the model.")
+tf.app.flags.DEFINE_integer("in_vocab_size", 12500, "input vocabulary size.")
+tf.app.flags.DEFINE_integer("out_vocab_size", 12500, "output vocabulary size.")
+tf.app.flags.DEFINE_string("data_dir", "./datas", "Data directory")
+tf.app.flags.DEFINE_string("train_dir", "./datas", "Training directory.")
+tf.app.flags.DEFINE_integer("max_train_data_size", 0,
+                            "Limit on the size of training data (0: no limit).")
+tf.app.flags.DEFINE_integer("steps_per_checkpoint", 100,
+                            "How many training steps to do per checkpoint.")
+tf.app.flags.DEFINE_boolean("decode", False,
+                            "Set to True for interactive decoding.")
+tf.app.flags.DEFINE_boolean("self_test", False,
+```
+```ここに言語を入力
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+"""Sequence-to-sequence model with an attention mechanism."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import random
+import numpy as np
+from six.moves import xrange
+import tensorflow as tf
+from tensorflow.models.rnn import rnn_cell
+from tensorflow.models.rnn import seq2seq
+import data_utils
+class Seq2SeqModel(object):
+  def __init__(self, source_vocab_size, target_vocab_size, buckets, size,
+               num_layers, max_gradient_norm, batch_size, learning_rate,
+               learning_rate_decay_factor, use_lstm=False,
+               num_samples=512, forward_only=False):
+    self.source_vocab_size = source_vocab_size
+    self.target_vocab_size = target_vocab_size
+    self.buckets = buckets
+    self.batch_size = batch_size
+    self.learning_rate = tf.Variable(float(learning_rate), trainable=False)
+    self.learning_rate_decay_op = self.learning_rate.assign(
+        self.learning_rate * learning_rate_decay_factor)
+    self.global_step = tf.Variable(0, trainable=False)
+    output_projection = None
+    softmax_loss_function = None
+    if num_samples > 0 and num_samples < self.target_vocab_size:
+      with tf.device("/cpu:0"):
+        w = tf.get_variable("proj_w", [size, self.target_vocab_size])
+        w_t = tf.transpose(w)
+        b = tf.get_variable("proj_b", [self.target_vocab_size])
+      output_projection = (w, b)
+      def sampled_loss(inputs, labels):
+        with tf.device("/cpu:0"):
+          labels = tf.reshape(labels, [-1, 1])
+          return tf.nn.sampled_softmax_loss(w_t, b, inputs, labels, num_samples,
+                                            self.target_vocab_size)
+      softmax_loss_function = sampled_loss
+    single_cell = rnn_cell.GRUCell(size)
+    if use_lstm:
+      single_cell = rnn_cell.BasicLSTMCell(size)
+    cell = single_cell
+    if num_layers > 1:
+      cell = rnn_cell.MultiRNNCell([single_cell] * num_layers)
+    def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
+      return seq2seq.embedding_attention_seq2seq(
+          encoder_inputs, decoder_inputs, cell, source_vocab_size,
+          target_vocab_size, output_projection=output_projection,
+          feed_previous=do_decode)
+    self.encoder_inputs = []
+    self.decoder_inputs = []
+    self.target_weights = []
+    for i in xrange(buckets[-1][0]):
+      self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
+                                                name="encoder{0}".format(i)))
+    for i in xrange(buckets[-1][1] + 1):
+      self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
+                                                name="decoder{0}".format(i)))
+      self.target_weights.append(tf.placeholder(tf.float32, shape=[None],
+                                                name="weight{0}".format(i)))
+    targets = [self.decoder_inputs[i + 1]
+               for i in xrange(len(self.decoder_inputs) - 1)]
+   ```