Neural Network with TensorFlow+TFLearn #1

Published on by anesth

MNISTデータセット　手書き0-9の機械認識
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In[1]
## 1.ライブラリの読み込み ##
# TensorFlowライブラリ
import tensorflow as tf
# tflearnライブラリ
import tflearn

# mnistデータセットを扱うためのライブラリ
import tflearn.datasets.mnist as mnist

# MNIST画像を表示するためのライブラリ
from matplotlib import pyplot as plt
from matplotlib import cm
import numpy as np
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In[2]
## 2.データの読み込みと前処理 ##
# MNISTデータを./data/mnistへダウンロードし、解凍して各変数へ格納
trainX, trainY, testX, testY = mnist.load_data(‘./data/mnist/’, one_hot=True)

Extracting ./data/mnist/train-images-idx3-ubyte.gz
Extracting ./data/mnist/train-labels-idx1-ubyte.gz
Extracting ./data/mnist/t10k-images-idx3-ubyte.gz
Extracting ./data/mnist/t10k-labels-idx1-ubyte.gz
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In[3]
## データの確認
# 学習用の画像ピクセルデータと正解データのサイズを確認
print(len(trainX),len(trainY))

# テスト用の画像ピクセルデータと正解データのサイズを確認
print(len(testX),len(testY))

# 学習用の画像ピクセルデータを確認
print(trainX)

# 学習用の正解データを確認
print(trainY)

out[3]
55000 55000
10000 10000
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…,
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[[ 0. 0. 0. …, 1. 0. 0.]
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…,
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[ 0. 0. 0. …, 0. 0. 0.]
[ 0. 0. 0. …, 0. 1. 0.]]
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In[4]
# 学習用の画像ピクセルデータを確認（1枚目）
trainX[0]

out[v4]
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In[5]
# 学習用の画像データを確認（1枚目）
plt.imshow(trainX[0].reshape(28, 28), cmap=cm.gray_r, interpolation=’nearest’)
plt.show()

out[5]
Unknown

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In[6]
# 学習用の正解データを確認（1枚目）
trainY[0]

out[6]
array([ 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.])
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In[7]
## 3.ニューラルネットワークの作成 ##

## 初期化
tf.reset_default_graph()

## 入力層の作成
net = tflearn.input_data(shape=[None, 784])

## 中間層の作成
net = tflearn.fully_connected(net, 128, activation=’relu’)
net = tflearn.dropout(net, 0.5)

## 出力層の作成
net = tflearn.fully_connected(net, 10, activation=’softmax’)
net = tflearn.regression(net, optimizer=’sgd’, learning_rate=0.5, loss=’categorical_crossentropy’)

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In[8]
## 4．モデルの作成（学習） ##
# 学習の実行
model = tflearn.DNN(net)
model.fit(trainX, trainY, n_epoch=20, batch_size=100, validation_set=0.1, show_metric=True)

Out[8]
Training Step: 9899 | total loss: 0.10077 | time: 3.300s
| SGD | epoch: 020 | loss: 0.10077 – acc: 0.9680 — iter: 49400/49500
Training Step: 9900 | total loss: 0.09962 | time: 4.334s
| SGD | epoch: 020 | loss: 0.09962 – acc: 0.9682 | val_loss: 0.08137 – val_acc: 0.9767 — iter: 49500/49500
—
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In[9]
# 5．モデルの適用（予測） ##
pred = np.array(model.predict(testX)).argmax(axis=1)
print(pred)

label = testY.argmax(axis=1)
print(label)

accuracy = np.mean(pred == label, axis=0)
print(accuracy)

Out[9]
[7 2 1 …, 4 5 6]
[7 2 1 …, 4 5 6]
0.976
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