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showImages(x_train[select_indices], t_train[select_indices], rows=4)
# Fashion-MNIST をダウンロードして変数に代入する
import tensorflow as tf
(x_train, t_train), (x_test, t_test) = tf.keras.datasets.fashion_mnist.load_data()
# データの形を確認する
print(x_train.shape)
print(t_train.shape)
print(x_test.shape)
print(t_test.shape)
(60000, 28, 28) (60000,) (10000, 28, 28) (10000,)
# ファッションアイテムの種類
class_names = [
'T-shirt/top',
'Trouser',
'Pullover',
'Dress',
'Coat',
'Sandal',
'Shirt',
'Sneaker',
'Bag',
'Ankle boot'
]
# x_train (60000個)からランダムに50個選んで表示する
import numpy as np
select_indices = np.random.choice(range(len(x_train)), 50)
%matplotlib inline
import matplotlib.pyplot as plt
def showImages(xs,ts,ts2=[],rows=-1,cols=-1,w=2.8,h=2.8):
N = len(xs)
if rows < 0: rows = 1
if cols < 0: cols = (N + rows -1) // rows
fig, ax = plt.subplots(rows, cols, figsize=(w*cols, h*rows*1.5))
idx = 0
for row in range(rows):
for col in range(cols) :
if idx < N:
if rows == 1 and cols == 1:
axis = ax
elif rows == 1:
axis = ax[col]
elif cols == 1:
axis = ax[row]
else:
axis = ax[row][col]
axis.imshow(xs[idx], cmap='gray')
axis.text(0.5, -0.25, class_names[ts[idx]],fontsize=12, ha='center', transform=axis.transAxes)
if len(ts2)>0:
axis.text(0.5, -0.5, class_names[ts2[idx]],fontsize=12, ha='center', transform=axis.transAxes)
axis.axis('off')
idx += 1
plt.show()
showImages(x_train[select_indices], t_train[select_indices], rows=4)
# 画像の解像度は 28x28 であるが、これを1次元ベクトル(要素数 28*28 = 784)に変換する。
# 各ピクセルの値は [0, 255] の値を持つ(0:黒, 255: 白, 128: 灰色)。これを[0.0, 1.0]の値に変換する。
x_train_rs = x_train.reshape((-1, 28*28)).astype('float32') / 255
x_test_rs = x_test.reshape((-1, 28*28)).astype('float32') / 255
print(x_train_rs.shape)
print(x_test_rs.shape)
(60000, 784) (10000, 784)
# 正解ラベル[0,9]をone-hot-vectorに変換する。【例】2 → (0, 0, 1, 0, 0, 0, 0, 0, 0, 0)
from tensorflow import keras
class_num = len(class_names)
t_train_cat = keras.utils.to_categorical(t_train, class_num)
t_test_cat = keras.utils.to_categorical(t_test, class_num)
print(t_train_cat.shape)
print(t_test_cat.shape)
(60000, 10) (10000, 10)
# ニューラルネットワークモデルを定義する
#from tensorflow import keras
def getModel(input_dim, output_dim):
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(256, activation='relu',input_shape=input_dim))
model.add(tf.keras.layers.Dense(output_dim, activation='softmax'))
return model
# ニューラルネットワークモデルを作成する
model = getModel((28*28,), 10)
# ニューラルネットワークモデルの構造を表示する
model.summary()
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 256) 200960 _________________________________________________________________ dense_1 (Dense) (None, 10) 2570 ================================================================= Total params: 203,530 Trainable params: 203,530 Non-trainable params: 0 _________________________________________________________________
# 誤差の計算方法と学習方法を設定する
# 誤差の計算方法: categorical cross entropy
# 学習方法: Adam, 学習率: 0.0005
model.compile(
loss='categorical_crossentropy',
optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),
metrics=['accuracy']
)
# 訓練データを「学習用データ」と「検証用データ」に 8:2 の比率で分ける
from sklearn.model_selection import train_test_split
x_fit, x_val, t_fit, t_val = train_test_split(x_train_rs, t_train_cat, test_size=0.2, random_state=42)
print(x_fit.shape)
print(x_val.shape)
print(t_fit.shape)
print(t_val.shape)
(48000, 784) (12000, 784) (48000, 10) (12000, 10)
# 学習する
history = model.fit(
x_fit, # 訓練データ
t_fit, # 訓練データの正解
batch_size=32, # 32個の訓練データごとに学習を進める
epochs=10, # 訓練データ全体について10回繰り返す
shuffle=True, # シャッフル
validation_data=(x_val, t_val) # epoch毎にテストデータで正解率を確認すt
)
Epoch 1/10 1500/1500 [==============================] - 4s 3ms/step - loss: 0.5250 - accuracy: 0.8176 - val_loss: 0.4275 - val_accuracy: 0.8462 Epoch 2/10 1500/1500 [==============================] - 4s 2ms/step - loss: 0.3943 - accuracy: 0.8594 - val_loss: 0.4215 - val_accuracy: 0.8487 Epoch 3/10 1500/1500 [==============================] - 4s 2ms/step - loss: 0.3510 - accuracy: 0.8725 - val_loss: 0.3440 - val_accuracy: 0.8760 Epoch 4/10 1500/1500 [==============================] - 4s 2ms/step - loss: 0.3203 - accuracy: 0.8841 - val_loss: 0.3509 - val_accuracy: 0.8711 Epoch 5/10 1500/1500 [==============================] - 3s 2ms/step - loss: 0.3005 - accuracy: 0.8899 - val_loss: 0.3321 - val_accuracy: 0.8798 Epoch 6/10 1500/1500 [==============================] - 4s 2ms/step - loss: 0.2840 - accuracy: 0.8941 - val_loss: 0.3337 - val_accuracy: 0.8787 Epoch 7/10 1500/1500 [==============================] - 4s 2ms/step - loss: 0.2683 - accuracy: 0.9006 - val_loss: 0.3269 - val_accuracy: 0.8808 Epoch 8/10 1500/1500 [==============================] - 4s 2ms/step - loss: 0.2572 - accuracy: 0.9045 - val_loss: 0.3156 - val_accuracy: 0.8842 Epoch 9/10 1500/1500 [==============================] - 4s 2ms/step - loss: 0.2453 - accuracy: 0.9107 - val_loss: 0.3100 - val_accuracy: 0.8912 Epoch 10/10 1500/1500 [==============================] - 4s 2ms/step - loss: 0.2371 - accuracy: 0.9130 - val_loss: 0.3156 - val_accuracy: 0.8852
# 学習過程を表示する関数
%matplotlib inline
import matplotlib.pyplot as plt
def plot_history(history):
fig, ax = plt.subplots(1, 2, figsize=(6*2.5, 4))
ax[0].plot(history['loss'], c='b', label='loss')
ax[0].plot(history['val_loss'], c='r', label='val_loss')
ax[0].legend(loc='upper right')
ax[0].set_xlabel('epochs')
ax[0].set_ylabel('loss')
ax[1].plot(history['accuracy'], c='b', label='acc')
ax[1].plot(history['val_accuracy'], c='r', label='val_acc')
ax[1].legend(loc='lower right')
ax[1].set_xlabel('epochs')
ax[1].set_ylabel('accuracy')
plt.subplots_adjust(hspace=0.5)
plt.show()
# 学習過程をグラフ表示する
plot_history(history.history)
# テスト用データで精度を確認する。
score = model.evaluate(x_test_rs, t_test_cat)
313/313 [==============================] - 1s 2ms/step - loss: 0.3410 - accuracy: 0.8819
# テスト用データから一部の画像を選んで、分類してみる
idxs = np.random.choice(range(len(x_test)), 50)
xs_rs = x_test_rs[idxs]
ys_cat = model(xs_rs)
ys = np.argmax(ys_cat, axis=1)
# テスト用データの一部の画像、予測、正解を表示する
showImages(x_test[idxs], ys, t_test[idxs], rows=5)
