モデルが行う「画像の分類」の内容は，画像が各類に属する確率である。
この表現形式の分類を，「prediction」と呼ぶ：
<![CDATA[>>> predictions = model.predict(test_images)]]>
最初の画像の prediction は：
<![CDATA[>>> predictions[0]
>>> array([1.0934007e-06, 2.5808379e-07, 4.1600650e-08, 5.9769114e-08,
6.9863950e-09, 2.5005983e-03, 8.4936289e-07, 2.8218931e-02,
1.3759811e-05, 9.6926415e-01], dtype=float32)]]>
一番確信度が高いラベルは：
<![CDATA[>>> import numpy as np
>>> np.argmax(predictions[0])
9
]]>
そして実際は：
当たりである。
prediction をグラフに表してみる
──最初の10枚の画像に対する prediction：
$ vi predictions.py
|
<![CDATA[#!/usr/bin/env python
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
# data
fashion_mnist = keras.datasets.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']
# image-preprocessing
train_images = train_images / 255.0
test_images = test_images / 255.0
# model setup
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# training
model.fit(train_images, train_labels, epochs=5)
# prediction
predictions = model.predict(test_images)
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = 'blue'
else:
color = 'red'
plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100*np.max(predictions_array),
class_names[true_label]),
color=color)
def plot_value_array(i, predictions_array, true_label):
predictions_array, true_label = predictions_array[i], true_label[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)
thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('blue')
# prediction : test-image 0 - 14
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2*num_cols, 2*i+1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(num_rows, 2*num_cols, 2*i+2)
plot_value_array(i, predictions, test_labels)
plt.show()]]>
|
$ chmod +x predictions.py
$ ./predictions.py
<![CDATA[Train on 60000 samples
Epoch 1/5
60000/60000 [==============================] - 25s 410us/sample - loss: 0.5004 - acc: 0.8240
Epoch 2/5
60000/60000 [==============================] - 24s 401us/sample - loss: 0.3721 - acc: 0.8659
Epoch 3/5
60000/60000 [==============================] - 24s 397us/sample - loss: 0.3397 - acc: 0.8759
Epoch 4/5
60000/60000 [==============================] - 24s 398us/sample - loss: 0.3110 - acc: 0.8855
Epoch 5/5
60000/60000 [==============================] - 24s 400us/sample - loss: 0.2969 - acc: 0.8914]]>
|
