構築したモデルに学習データを投入して学習させてみよう．Keras では model.fit() を呼び出すだけで学習ができます．
学習させる (06-train.py)
import numpy as np
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense
# ファイルを開いて読み込む
x_train = np.load('train_X_data.npy')
y_train = np.load('train_Y_data.npy')
x_test = np.load('test_X_data.npy')
y_test = np.load('test_Y_data.npy')
# 正解ラベルを one-hot-encoding にする
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)
# モデルを作る
model = Sequential()
model.add(Dense(128, activation='relu', input_dim=225)) # input_dim = 15 x 15 = 225
model.add(Dense(10, activation='softmax'))
# モデルをコンパイルする
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# 学習してみよう（このコードだけで，学習状況も表示される）
model.fit(x_train, y_train,
batch_size=20,
epochs=30,
verbose=1)
Using TensorFlow backend. Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 128) 28928 _________________________________________________________________ dense_2 (Dense) (None, 10) 1290 ================================================================= Total params: 30,218 Trainable params: 30,218 Non-trainable params: 0 _________________________________________________________________ Epoch 1/30 80/80 [==============================] - 0s 2ms/step - loss: 2.3014 - accuracy: 0.1625 Epoch 2/30 80/80 [==============================] - 0s 62us/step - loss: 1.7425 - accuracy: 0.5000 Epoch 3/30 80/80 [==============================] - 0s 237us/step - loss: 1.4521 - accuracy: 0.6625 Epoch 4/30 80/80 [==============================] - 0s 125us/step - loss: 1.2113 - accuracy: 0.8500 Epoch 5/30 80/80 [==============================] - 0s 100us/step - loss: 1.0363 - accuracy: 0.8750 Epoch 6/30 80/80 [==============================] - 0s 100us/step - loss: 0.8712 - accuracy: 0.8875 Epoch 7/30 80/80 [==============================] - 0s 100us/step - loss: 0.7511 - accuracy: 0.9125 Epoch 8/30 80/80 [==============================] - 0s 100us/step - loss: 0.6510 - accuracy: 0.9375 Epoch 9/30 80/80 [==============================] - 0s 100us/step - loss: 0.5552 - accuracy: 0.9250 Epoch 10/30 80/80 [==============================] - 0s 112us/step - loss: 0.4797 - accuracy: 0.9625 Epoch 11/30 80/80 [==============================] - 0s 112us/step - loss: 0.4069 - accuracy: 0.9875 Epoch 12/30 80/80 [==============================] - 0s 112us/step - loss: 0.3509 - accuracy: 1.0000 Epoch 13/30 80/80 [==============================] - 0s 100us/step - loss: 0.3064 - accuracy: 0.9750 Epoch 14/30 80/80 [==============================] - 0s 100us/step - loss: 0.2646 - accuracy: 1.0000 Epoch 15/30 80/80 [==============================] - 0s 112us/step - loss: 0.2298 - accuracy: 1.0000 Epoch 16/30 80/80 [==============================] - 0s 112us/step - loss: 0.1951 - accuracy: 1.0000 Epoch 17/30 80/80 [==============================] - 0s 87us/step - loss: 0.1712 - accuracy: 1.0000 Epoch 18/30 80/80 [==============================] - 0s 87us/step - loss: 0.1463 - accuracy: 1.0000 Epoch 19/30 80/80 [==============================] - 0s 100us/step - loss: 0.1263 - accuracy: 1.0000 Epoch 20/30 80/80 [==============================] - 0s 100us/step - loss: 0.1136 - accuracy: 1.0000 Epoch 21/30 80/80 [==============================] - 0s 87us/step - loss: 0.0980 - accuracy: 1.0000 Epoch 22/30 80/80 [==============================] - 0s 237us/step - loss: 0.0820 - accuracy: 1.0000 Epoch 23/30 80/80 [==============================] - 0s 224us/step - loss: 0.0763 - accuracy: 1.0000 Epoch 24/30 80/80 [==============================] - 0s 125us/step - loss: 0.0626 - accuracy: 1.0000 Epoch 25/30 80/80 [==============================] - 0s 100us/step - loss: 0.0584 - accuracy: 1.0000 Epoch 26/30 80/80 [==============================] - 0s 100us/step - loss: 0.0471 - accuracy: 1.0000 Epoch 27/30 80/80 [==============================] - 0s 100us/step - loss: 0.0427 - accuracy: 1.0000 Epoch 28/30 80/80 [==============================] - 0s 100us/step - loss: 0.0356 - accuracy: 1.0000 Epoch 29/30 80/80 [==============================] - 0s 100us/step - loss: 0.0314 - accuracy: 1.0000 Epoch 30/30 80/80 [==============================] - 0s 112us/step - loss: 0.0272 - accuracy: 1.0000
80個のデータを20個ずつに分けて，誤差が小さくなるようにパラメータを最適化します．その後，誤差 (loss) とその時の認識精度 (accuracy) が得られます．最初の学習（エポック）では loss が 2.3014，accuracy が 16.25% になりました．この学習を30回繰り返すことで，徐々に誤差が小さくなり，認識精度が向上していく様子が読み取れます．最終的には学習データを100%の精度で認識できるようになりました．なお，データの作成時に並び順をランダムにシャッフルしているので，結果は必ずしも一致しないことに注意してください．