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私はMNISTデータセットのTensorflow LSTMの例で遊んでいます。 私が理解していないのは、最終層でロジスティック回帰が使われている理由です。 LSTMネットワークの最終出力は、以前の「タイムステップ」の出力を使用するよりも良い見積もりではありませんか？ LSTMネットワークの最後の出力を分類に使用するにはどうすればよいですか？Tensorflow MNISTのLSTMの最終層例
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""
This example builds rnn network for mnist data.
Borrowed structure from here: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/recurrent_network.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn import metrics, preprocessing
import tensorflow as tf
from tensorflow.contrib import learn
# Parameters
learning_rate = 0.1
training_steps = 3000
batch_size = 128
# Network Parameters
n_input = 28 # MNIST data input (img shape: 28*28)
rnn_timesteps = 28 # timesteps
n_hidden = 128 # hidden layer num of features
n_classes = 10 # MNIST total classes (0-9 digits)
### Download and load MNIST data.
mnist = learn.datasets.load_dataset('mnist')
X_train = mnist.train.images
y_train = mnist.train.labels
X_test = mnist.test.images
y_test = mnist.test.labels
print(X_train.shape) # (55000, 784)
print(y_train.shape) # (55000,)
# It's useful to scale to ensure Stochastic Gradient Descent will do the right thing
scaler = preprocessing.StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
def rnn_model(X, y):
X = tf.reshape(X, [-1, rnn_timesteps, n_input]) # (batch_size, rnn_timesteps, n_input)
# # permute rnn_timesteps and batch_size
X = tf.transpose(X, [1, 0, 2])
# # Reshape to prepare input to hidden activation
X = tf.reshape(X, [-1, n_input]) # (rnn_timesteps*batch_size, n_input)
# # Split data because rnn cell needs a list of inputs for the RNN inner loop
X = tf.split(0, rnn_timesteps, X) # rnn_timesteps * (batch_size, n_input)
# Define a GRU cell with tensorflow
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden)
# Get lstm cell output
_, encoding = tf.nn.rnn(lstm_cell, X, dtype=tf.float32)
return learn.models.logistic_regression(encoding, y)
classifier = learn.TensorFlowEstimator(model_fn=rnn_model, n_classes=n_classes,
batch_size=batch_size,
steps=training_steps,
learning_rate=learning_rate)
classifier.fit(X_train, y_train, logdir="/tmp/mnist_rnn")
score = metrics.accuracy_score(y_test, classifier.predict(X_test))
print('Accuracy: {0:f}'.format(score))