はじめてのニューラルネットワーク：分類問題の初歩

# TensorFlow and tf.keras
import tensorflow as tf

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

print(tf.__version__)

2022-12-15 00:31:21.895161: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-15 00:31:21.895268: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-15 00:31:21.895278: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2.11.0

fashion_mnist = tf.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']

train_images.shape

(60000, 28, 28)

len(train_labels)

60000

train_labels

array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)

test_images.shape

(10000, 28, 28)

len(test_labels)

10000

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()

train_images = train_images / 255.0

test_images = test_images / 255.0

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()

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

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=10)

Epoch 1/10
1875/1875 [==============================] - 5s 2ms/step - loss: 0.5022 - accuracy: 0.8239
Epoch 2/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.3784 - accuracy: 0.8636
Epoch 3/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.3419 - accuracy: 0.8745
Epoch 4/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.3157 - accuracy: 0.8838
Epoch 5/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.2996 - accuracy: 0.8889
Epoch 6/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.2831 - accuracy: 0.8948
Epoch 7/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.2700 - accuracy: 0.8989
Epoch 8/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.2583 - accuracy: 0.9038
Epoch 9/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.2489 - accuracy: 0.9079
Epoch 10/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.2407 - accuracy: 0.9096
<keras.callbacks.History at 0x7f659e338ee0>

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

print('\nTest accuracy:', test_acc)

313/313 - 1s - loss: 0.3393 - accuracy: 0.8804 - 635ms/epoch - 2ms/step

Test accuracy: 0.8804000020027161

probability_model = tf.keras.Sequential([model, 
                                         tf.keras.layers.Softmax()])

predictions = probability_model.predict(test_images)

313/313 [==============================] - 0s 1ms/step

predictions[0]

array([5.6910987e-09, 2.4519869e-07, 5.8553162e-08, 3.0318517e-08,
       3.1033121e-08, 3.6219638e-04, 2.5309854e-08, 1.5150005e-02,
       8.6693162e-06, 9.8447871e-01], dtype=float32)

np.argmax(predictions[0])

9

test_labels[0]

9

def plot_image(i, predictions_array, true_label, img):
  true_label, img = 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):
  true_label = true_label[i]
  plt.grid(False)
  plt.xticks(range(10))
  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')

i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i],  test_labels)
plt.show()

i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i],  test_labels)
plt.show()

# Plot the first X test images, their predicted labels, and the true labels.
# Color correct predictions in blue and incorrect predictions in red.
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[i], test_labels, test_images)
  plt.subplot(num_rows, 2*num_cols, 2*i+2)
  plot_value_array(i, predictions[i], test_labels)
plt.tight_layout()
plt.show()

# Grab an image from the test dataset.
img = test_images[1]

print(img.shape)

(28, 28)

# Add the image to a batch where it's the only member.
img = (np.expand_dims(img,0))

print(img.shape)

(1, 28, 28)

predictions_single = probability_model.predict(img)

print(predictions_single)

1/1 [==============================] - 0s 24ms/step
[[2.4473981e-04 1.2069421e-11 9.9653888e-01 2.1565708e-14 1.3155717e-03
  2.2960410e-12 1.9008714e-03 8.9515721e-13 2.3349067e-10 3.9004930e-16]]

plot_value_array(1, predictions_single[0], test_labels)
_ = plt.xticks(range(10), class_names, rotation=45)
plt.show()

np.argmax(predictions_single[0])

2

# MIT License
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# Copyright (c) 2017 François Chollet
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