'''Trains a simple convnet on the MNIST dataset.

Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function

import json
import numpy

import keras
from keras import backend as K
from keras.datasets import fashion_mnist
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential

batch_size = 128
num_classes = 10
epochs = 100

# input image dimensions
img_rows, img_cols = 28, 28

hidden_layers = ["second", "third"]


class Gaussian(keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        for hidden_layer in hidden_layers:
            params = self.model.get_layer(hidden_layer).get_weights()

            weights = params[0]
            biases = params[1]

            weight_signs = numpy.sign(weights)
            bias_signs = numpy.sign(biases)

            weight_noise = numpy.random.uniform(0, 1, weights.shape) * 0.0003
            weight_noise = weight_noise * weight_signs
            weight_noise = numpy.add(weight_noise, weights)
            params[0] = weight_noise

            bias_noise = numpy.random.uniform(0, 1, biases.shape) * 0.0002
            bias_noise = bias_noise * bias_signs
            bias_noise = numpy.add(bias_noise, biases)
            params[1] = bias_noise

            self.model.get_layer(hidden_layer).set_weights(params)


# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(64, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(256, (3, 3), activation='relu', name="second"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

model.add(Conv2D(256, (3, 3), activation='relu', name="third"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer="adam",
              metrics=['accuracy'])

gaussian_noise = Gaussian()
history = model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    callbacks=[gaussian_noise],
                    validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# plt.plot(history.history['acc'])
# plt.plot(history.history['val_acc'])
# plt.title('Model accuracy')
# plt.ylabel('Accuracy')
# plt.xlabel('Epoch')
# plt.legend(['Train', 'Test'], loc='upper left')
# plt.savefig('fashion_mnist_cnn_noise_adam.png')
print(history.history)
with open('fashion_mnist_cnn_noise_adam.json', 'w') as f:
    json.dump(history.history, f)

# plot_model(model, to_file='fashion_mnist_cnn_architecture.png')

'''Trains a simple convnet on the MNIST dataset.

Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function

import json
import numpy

import keras
from keras import backend as K
from keras.datasets import fashion_mnist
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential

batch_size = 128
num_classes = 10
epochs = 100

# input image dimensions
img_rows, img_cols = 28, 28

hidden_layers = ["second", "third"]


class Gaussian(keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        for hidden_layer in hidden_layers:
            params = self.model.get_layer(hidden_layer).get_weights()

            weights = params[0]
            biases = params[1]

            weight_signs = numpy.sign(weights)
            bias_signs = numpy.sign(biases)

            weight_noise = numpy.random.uniform(0, 1, weights.shape) * 0.0003
            weight_noise = weight_noise * weight_signs
            weight_noise = numpy.add(weight_noise, weights)
            params[0] = weight_noise

            bias_noise = numpy.random.uniform(0, 1, biases.shape) * 0.0002
            bias_noise = bias_noise * bias_signs
            bias_noise = numpy.add(bias_noise, biases)
            params[1] = bias_noise

            self.model.get_layer(hidden_layer).set_weights(params)


# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(64, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(256, (3, 3), activation='relu', name="second"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

model.add(Conv2D(256, (3, 3), activation='relu', name="third"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))

model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer="adam",
              metrics=['accuracy'])

gaussian_noise = Gaussian()
history = model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    callbacks=[gaussian_noise],
                    validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# plt.plot(history.history['acc'])
# plt.plot(history.history['val_acc'])
# plt.title('Model accuracy')
# plt.ylabel('Accuracy')
# plt.xlabel('Epoch')
# plt.legend(['Train', 'Test'], loc='upper left')
# plt.savefig('fashion_mnist_cnn_noise_adam.png')
print(history.history)
with open('fashion_mnist_cnn_noise_adam.json', 'w') as f:
    json.dump(history.history, f)

# plot_model(model, to_file='fashion_mnist_cnn_architecture.png')

Trains a simple convnet on the MNIST dataset.  Gets to 99.25% test accuracy after 12 epochs (there is still a lot of margin for parameter tuning). 16 seconds per epoch on a GRID K520 GPU. # input image dimensions # the data, split between train and test sets # convert class vectors to binary class matrices # plt.plot(history.history['acc']) # plt.plot(history.history['val_acc']) # plt.title('Model accuracy') # plt.ylabel('Accuracy') # plt.xlabel('Epoch') # plt.legend(['Train', 'Test'], loc='upper left') # plt.savefig('fashion_mnist_cnn_noise_adam.png') # plot_model(model, to_file='fashion_mnist_cnn_architecture.png')