is1394 · September 1, 2017 02:56
diff --git a/mnist_example.py b/mnist_example.py
 # [define network model]
 from keras.models import Sequential
 from keras.layers import Activation, Dense, Dropout, Flatten
 from keras.layers.convolutional import Convolution2D, MaxPooling2D

 model = Sequential()
 # count of convolution filter=32, row of filter=3, col of filter=3, 
 # note: input_shape format is different by the backend: tensorflow or theano
 #       tensorflow: (rows, cols, channels)  # number of color channels
 model.add(Convolution2D(32, 3, 3, input_shape=(28, 28, 1)))
 model.add(Activation("relu"))
 model.add(Convolution2D(64, 3, 3))
 model.add(Activation("relu"))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Dropout(0.5))

 model.add(Flatten())
 model.add(Dense(128))
 model.add(Activation("relu"))
 model.add(Dropout(0.5))

 model.add(Dense(10))
 model.add(Activation("softmax"))

 model.compile(
    optimizer="sgd", loss="categorical_crossentropy", metrics=['accuracy'])
 print(model.to_yaml()) # show model


 # [prepare dataset]
 from keras.datasets import mnist
 from keras.utils import np_utils

 # mnist dataset as monoral image and its number
 # x: 0-255 of 28x28, y: 0-9, train: 60000 x and y, test: 10000 x and y
 (x_train_raw, y_train_raw), (x_test_raw, y_test_raw) = mnist.load_data()

 # convert (samples, rows, cols) to (samples, rows, cols, channels)
 x_train = x_train_raw.reshape(*x_train_raw.shape, 1).astype("float32") / 255
 x_test = x_test_raw.reshape(*x_test_raw.shape, 1).astype("float32") / 255
 # convert 0-9 data to 10 categorical array: e.g. 2 => [0.0.1,0.0, 0,0,0,0,0]
 y_train = np_utils.to_categorical(y_train_raw, 10)
 y_test = np_utils.to_categorical(y_test_raw, 10)


 # [train by train dataset] wait an hour
 history = model.fit(x_train, y_train, validation_data=(x_test, y_test))


 # [check by test dataset]
 score = model.evaluate(x_test, y_test)
 print("[score] {}, accuracy: {}".format(*score))


 # [predict by single data]
 result = model.predict(x_test[0:1]) # numpy array of a single data
 print("[result] {} may be {}".format(result[0], result[0].argmax()))
 print("[answer] {} as {}".format(y_test[0], y_test_raw[0]))


 #############################################################################
 # [setup keras]
 # $ python3 -m venv keras
 # $ ./keras/bin/pip install six
 # $ ./keras/bin/pip install https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py3-none-any.whl
 # $ ./keras/bin/pip install keras
 # $ ./keras/bin/pip install h5py # for save and load model weight
 #
 # $ ./keras/bin/python3 mnist_example.py
	# [define network model]
	from keras.models import Sequential
	from keras.layers import Activation, Dense, Dropout, Flatten
	from keras.layers.convolutional import Convolution2D, MaxPooling2D

	model = Sequential()
	# count of convolution filter=32, row of filter=3, col of filter=3,
	# note: input_shape format is different by the backend: tensorflow or theano
	# tensorflow: (rows, cols, channels) # number of color channels
	model.add(Convolution2D(32, 3, 3, input_shape=(28, 28, 1)))
	model.add(Activation("relu"))
	model.add(Convolution2D(64, 3, 3))
	model.add(Activation("relu"))
	model.add(MaxPooling2D(pool_size=(2, 2)))
	model.add(Dropout(0.5))

	model.add(Flatten())
	model.add(Dense(128))
	model.add(Activation("relu"))
	model.add(Dropout(0.5))

	model.add(Dense(10))
	model.add(Activation("softmax"))

	model.compile(
	optimizer="sgd", loss="categorical_crossentropy", metrics=['accuracy'])
	print(model.to_yaml()) # show model


	# [prepare dataset]
	from keras.datasets import mnist
	from keras.utils import np_utils

	# mnist dataset as monoral image and its number
	# x: 0-255 of 28x28, y: 0-9, train: 60000 x and y, test: 10000 x and y
	(x_train_raw, y_train_raw), (x_test_raw, y_test_raw) = mnist.load_data()

	# convert (samples, rows, cols) to (samples, rows, cols, channels)
	x_train = x_train_raw.reshape(*x_train_raw.shape, 1).astype("float32") / 255
	x_test = x_test_raw.reshape(*x_test_raw.shape, 1).astype("float32") / 255
	# convert 0-9 data to 10 categorical array: e.g. 2 => [0.0.1,0.0, 0,0,0,0,0]
	y_train = np_utils.to_categorical(y_train_raw, 10)
	y_test = np_utils.to_categorical(y_test_raw, 10)


	# [train by train dataset] wait an hour
	history = model.fit(x_train, y_train, validation_data=(x_test, y_test))


	# [check by test dataset]
	score = model.evaluate(x_test, y_test)
	print("[score] {}, accuracy: {}".format(*score))


	# [predict by single data]
	result = model.predict(x_test[0:1]) # numpy array of a single data
	print("[result] {} may be {}".format(result[0], result[0].argmax()))
	print("[answer] {} as {}".format(y_test[0], y_test_raw[0]))


	#############################################################################
	# [setup keras]
	# $ python3 -m venv keras
	# $ ./keras/bin/pip install six
	# $ ./keras/bin/pip install https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py3-none-any.whl
	# $ ./keras/bin/pip install keras
	# $ ./keras/bin/pip install h5py # for save and load model weight
	#
	# $ ./keras/bin/python3 mnist_example.py