ディープラーニングでアスキーアートを作る ref: http://qiita.com/OsciiArt/items/325714d8ab3f2b482ced

file0.py

def DeepAA(num_label=615, drop_out=0.5, weight_decay=0.001, input_shape = [64, 64]):
    """
    Build Deep Neural Network.
    :param num_label: int, number of classes, equal to candidates of characters
    :param drop_out:  float
    :param weight_decay: float
    :return: 
    """
    reg = l2(weight_decay)
    imageInput = Input(shape=input_shape)
    x = Reshape([input_shape[0], input_shape[1], 1])(imageInput)
    x = GaussianNoise(0.1)(x)
    x = Convolution2D(16, 3, 3, border_mode='same', W_regularizer=reg, b_regularizer=reg, init=normal)(x)
    x = BatchNormalization(axis=-3)(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=(2, 2), border_mode='same')(x)
    x = Dropout(drop_out)(x)
    x = Convolution2D(32, 3, 3, border_mode='same', W_regularizer=reg, b_regularizer=reg, init=normal)(x)
    x = BatchNormalization(axis=-3)(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=(2, 2), border_mode='same')(x)
    x = Dropout(drop_out)(x)
    x = Convolution2D(64, 3, 3, border_mode='same', W_regularizer=reg, b_regularizer=reg, init=normal)(x)
    x = BatchNormalization(axis=-3)(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=(2, 2), border_mode='same')(x)
    x = Dropout(drop_out)(x)
    x = Convolution2D(128, 3, 3, border_mode='same', W_regularizer=reg, b_regularizer=reg, init=normal)(x)
    x = BatchNormalization(axis=-3)(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=(2, 2), border_mode='same')(x)
    x = Flatten()(x)
    x = Dropout(drop_out)(x)
    y = Dense(num_label, activation='softmax')(x)

    model = Model(input=imageInput, output=y)

    return model

file0.txt

def DeepAA(num_label=615, drop_out=0.5, weight_decay=0.001, input_shape = [64, 64]):
    """
    Build Deep Neural Network.
    :param num_label: int, number of classes, equal to candidates of characters
    :param drop_out:  float
    :param weight_decay: float
    :return: 
    """
    reg = l2(weight_decay)
    imageInput = Input(shape=input_shape)
    x = Reshape([input_shape[0], input_shape[1], 1])(imageInput)
    x = GaussianNoise(0.1)(x)
    x = Convolution2D(16, 3, 3, border_mode='same', W_regularizer=reg, b_regularizer=reg, init=normal)(x)
    x = BatchNormalization(axis=-3)(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=(2, 2), border_mode='same')(x)
    x = Dropout(drop_out)(x)
    x = Convolution2D(32, 3, 3, border_mode='same', W_regularizer=reg, b_regularizer=reg, init=normal)(x)
    x = BatchNormalization(axis=-3)(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=(2, 2), border_mode='same')(x)
    x = Dropout(drop_out)(x)
    x = Convolution2D(64, 3, 3, border_mode='same', W_regularizer=reg, b_regularizer=reg, init=normal)(x)
    x = BatchNormalization(axis=-3)(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=(2, 2), border_mode='same')(x)
    x = Dropout(drop_out)(x)
    x = Convolution2D(128, 3, 3, border_mode='same', W_regularizer=reg, b_regularizer=reg, init=normal)(x)
    x = BatchNormalization(axis=-3)(x)
    x = Activation('relu')(x)
    x = MaxPooling2D(pool_size=(2, 2), border_mode='same')(x)
    x = Flatten()(x)
    x = Dropout(drop_out)(x)
    y = Dense(num_label, activation='softmax')(x)

    model = Model(input=imageInput, output=y)

    return model