Keras/Tensorflowで始めるディープラーニングの基礎 ref: http://qiita.com/yampy/items/975f28594f295702ef18

file0.txt

pip install --upgrade tensorflow
pip install keras

file1.txt

from keras.models import Sequential
from keras.layers import Dense, Activation

# モデルの作成
model = Sequential()

# モデルにレイヤーを積み上げていく
model.add(Dense(units=64, input_dim=100))
model.add(Activation('relu'))
model.add(Dense(units=10))
model.add(Activation('softmax'))

# 訓練プロセスの定義
model.compile(loss='categorical_crossentropy',
              optimizer='sgd',
              metrics=['accuracy'])

# 訓練の実行
# (x_train, y_trainはNumpy行列の学習データ)
model.fit(x_train, y_train, epochs=5, batch_size=32)

# 予測の実行
classes = model.predict(x_test, batch_size=128)

file14.txt

Train on 60000 samples, validate on 10000 samples
Epoch 1/10
59904/60000 [============================>.] - ETA: 0s - loss: 0.3267 - acc: 0.9009Epoch 00000: saving model to ./out/checkpoints/weights.00-0.08.hdf5
60000/60000 [==============================] - 135s - loss: 0.3263 - acc: 0.9011 - val_loss: 0.0785 - val_acc: 0.9749
Epoch 2/10
59904/60000 [============================>.] - ETA: 0s - loss: 0.1132 - acc: 0.9671Epoch 00001: saving model to ./out/checkpoints/weights.01-0.06.hdf5
60000/60000 [==============================] - 131s - loss: 0.1131 - acc: 0.9671 - val_loss: 0.0580 - val_acc: 0.9811
Epoch 3/10
59904/60000 [============================>.] - ETA: 0s - loss: 0.0871 - acc: 0.9735Epoch 00002: saving model to ./out/checkpoints/weights.02-0.05.hdf5
60000/60000 [==============================] - 135s - loss: 0.0870 - acc: 0.9735 - val_loss: 0.0469 - val_acc: 0.9850
(中略)
Epoch 10/10
59904/60000 [============================>.] - ETA: 0s - loss: 0.0410 - acc: 0.9876Epoch 00009: saving model to ./out/checkpoints/weights.09-0.03.hdf5
60000/60000 [==============================] - 140s - loss: 0.0410 - acc: 0.9876 - val_loss: 0.0295 - val_acc: 0.9902

file15.txt

tensorboard --logdir=./out/

file2.txt

from keras.layers import Input, Dense
from keras.models import Model

# インプットの定義
inputs = Input(shape=(784,))

# レイヤーの定義
nw = Dense(64, activation='relu')(inputs)
nw = Dense(64, activation='relu')(x)
predictions = Dense(10, activation='softmax')(x)

# モデルの定義(インプットとレイヤーを指定)
model = Model(inputs=inputs, outputs=predictions)

# 訓練プロセスの定義
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

#  訓練の実行
model.fit(data, labels)

# 予測の実行
classes = model.predict(x_test, batch_size=128)

file7.txt

Train on 60000 samples, validate on 10000 samples
Epoch 1/20
60000/60000 [==============================] - 7s - loss: 0.3319 - acc: 0.8974 - val_loss: 0.1267 - val_acc: 0.9607
Epoch 2/20
60000/60000 [==============================] - 6s - loss: 0.1621 - acc: 0.9524 - val_loss: 0.0921 - val_acc: 0.9733
Epoch 3/20
60000/60000 [==============================] - 6s - loss: 0.1318 - acc: 0.9610 - val_loss: 0.0874 - val_acc: 0.9750
(中略)
Epoch 20/20
60000/60000 [==============================] - 6s - loss: 0.0722 - acc: 0.9823 - val_loss: 0.1083 - val_acc: 0.9817

keras_mnist_cnn.ipynb

keras_mnist_simpleLayers.ipynb