Keras - Char 74k字符识别 - CNN

Question

我使用以下博客进行了使用CNN的字符识别。 http://ankivil.com/kaggle-first-steps-with-julia-chars74k-first-place-using-convolutional-neural-networks

我做的唯一改变是dim_ordering =“th”最新的keras兼容性。

model = Sequential()

    model.add(Convolution2D(128, 3, 3, border_mode='same', init='he_normal', activation = 'relu', input_shape=(1, img_rows, img_cols)))
    print model.output_shape
    model.add(Convolution2D(128, 3, 3, border_mode='same', init='he_normal', activation = 'relu'))
    print model.output_shape

    model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))
    print model.output_shape

    model.add(Convolution2D(256, 3, 3, border_mode='same', init='he_normal', activation = 'relu'))
    print model.output_shape
    model.add(Convolution2D(256, 3, 3, border_mode='same', init='he_normal', activation = 'relu'))
    print model.output_shape

    model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))
    print model.output_shape

    model.add(Convolution2D(512, 3, 3, border_mode='same', init='he_normal', activation = 'relu'))
    print model.output_shape
    model.add(Convolution2D(512, 3, 3, border_mode='same', init='he_normal', activation = 'relu'))
    print model.output_shape
    model.add(Convolution2D(512, 3, 3, border_mode='same', init='he_normal', activation = 'relu'))
    print model.output_shape

    model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="th"))
    print model.output_shape

    model.add(Flatten())
    print model.output_shape
    model.add(Dense(4096, init='he_normal', activation = 'relu'))
    print model.output_shape
    model.add(Dropout(0.5))
    print model.output_shape
    model.add(Dense(4096, init='he_normal', activation = 'relu'))
    print model.output_shape
    model.add(Dropout(0.5))
    print model.output_shape
    model.add(Dense(nb_classes, init='he_normal', activation = 'softmax'))
    print model.output_shape

在50-60次迭代后，我得到的精度非常差，为0.07，并且卡在那里。

你能为我提一些建议吗？我对使用CNN执行OCR的其他模型持开放态度。

谢谢， 希瓦