機械学習のココロ（Vol.56掲載）

著者：石井一夫

今回は、人工知能プラットフォーム「TensorFlow」のインストール手順と、TensorFlow を使った機械学習と画像分析の一例を紹介します。TensorFlow をベースにした機械学習環境の構築や利用には、やや難易度の高い作業がかつては必要でした。しかし最近、ニューラルネットワークライブラリの「Keras」が開発されたことで、非常に楽になっています。

シェルスクリプトマガジン Vol.56は以下のリンク先でご購入できます。

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt

print(tf.__version__)

# TensorFlow and tf.keras

import tensorflow as tf

from tensorflow import keras

# Helper libraries

import numpy as np

import matplotlib.pyplot as plt

print(tf.__version__)

from keras.datasets import fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train_images / 255.0
test_images = test_images / 255.0

from keras.datasets import fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',

'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

train_images = train_images / 255.0

test_images = test_images / 255.0

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])

model.compile(optimizer=tf.train.AdamOptimizer(), 
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model = keras.Sequential([

keras.layers.Flatten(input_shape=(28, 28)),

keras.layers.Dense(128, activation=tf.nn.relu),

keras.layers.Dense(10, activation=tf.nn.softmax)

])

model.compile(optimizer=tf.train.AdamOptimizer(),

loss='sparse_categorical_crossentropy',

metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=5)

test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)

model.fit(train_images, train_labels, epochs=5)

test_loss, test_acc = model.evaluate(test_images, test_labels)

print('Test accuracy:', test_acc)

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid('off')
    plt.imshow(test_images[i], cmap=plt.cm.binary)
    predicted_label = np.argmax(predictions[i])
    true_label = test_labels[i]
    if predicted_label == true_label:
      color = 'green'
    else:
      color = 'red'
    plt.xlabel("{} ({})".format(class_names[predicted_label],
                                  class_names[true_label]),
                                  color=color)

plt.figure(figsize=(10,10))

for i in range(25):

plt.subplot(5,5,i+1)

plt.xticks([])

plt.yticks([])

plt.grid('off')

plt.imshow(test_images[i], cmap=plt.cm.binary)

predicted_label = np.argmax(predictions[i])

true_label = test_labels[i]

if predicted_label == true_label:

color = 'green'

else:

color = 'red'

plt.xlabel("{} ({})".format(class_names[predicted_label],

class_names[true_label]),

color=color)