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

batch_size = 32
num_classes = 2
epochs = 12

idx_fold = 0
col_p1 = 3
output_dir = 'data/fold_' + str(idx_fold) + '/col/' + str(col_p1-3) + '/'
x_train = pickle.load(open(output_dir + 'X_train_l.pickle', 'rb'))
y_train = pickle.load(open(output_dir + 'Y_train_l.pickle', 'rb'))

# print(y_train)
# x_train = x_train[:int(0.8*x_train.shape[0]),:,:]
# x_val = x_train[int(0.8*x_train.shape[0]):,:,:]
# y_train = y_train[:int(0.8*y_train.shape[0])]
# y_val = y_train[int(0.8*y_train.shape[0]):]
# print(y_val)
# x_test = pickle.load(open(output_dir + 'X_test.pickle', 'rb'))
# y_test = pickle.load(open(output_dir + 'Y_test.pickle', 'rb'))

x_test = pickle.load(open(output_dir + 'X_test.pickle', 'rb'))
y_test = pickle.load(open(output_dir + 'Y_test.pickle', 'rb'))
# print(y_test)

img_rows, img_cols = x_train.shape[1], x_train.shape[2]
# input image dimensions
# img_rows, img_cols = 28, 28

# the data, shuffled and split between train and test sets
# (x_train, y_train), (x_test, y_test) = 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')
# print(y_train[:100])

# 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(32, kernel_size=(3, img_cols),
                 activation='relu',
                 input_shape=input_shape,
                 strides=1))
model.add(Conv2D(64, (3, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

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

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          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])
c65d65621e4dc12b3b1a5e49d89a8d62f2f728a6
<mask token>

<mask token>
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
<mask token>
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
<mask token>
model.add(Conv2D(32, kernel_size=(3, img_cols), activation='relu',
    input_shape=input_shape, strides=1))
model.add(Conv2D(64, (3, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.
    optimizers.Adadelta(), metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1,
    validation_data=(x_test, y_test))
<mask token>
print('Test loss:', score[0])
print('Test accuracy:', score[1])

<mask token>
batch_size = 32
num_classes = 2
epochs = 12
idx_fold = 0
col_p1 = 3
output_dir = 'data/fold_' + str(idx_fold) + '/col/' + str(col_p1 - 3) + '/'
x_train = pickle.load(open(output_dir + 'X_train_l.pickle', 'rb'))
y_train = pickle.load(open(output_dir + 'Y_train_l.pickle', 'rb'))
x_test = pickle.load(open(output_dir + 'X_test.pickle', 'rb'))
y_test = pickle.load(open(output_dir + 'Y_test.pickle', 'rb'))
img_rows, img_cols = x_train.shape[1], x_train.shape[2]
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')
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
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(32, kernel_size=(3, img_cols), activation='relu',
    input_shape=input_shape, strides=1))
model.add(Conv2D(64, (3, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.
    optimizers.Adadelta(), metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1,
    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])

<mask token>
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import pickle
batch_size = 32
num_classes = 2
epochs = 12
idx_fold = 0
col_p1 = 3
output_dir = 'data/fold_' + str(idx_fold) + '/col/' + str(col_p1 - 3) + '/'
x_train = pickle.load(open(output_dir + 'X_train_l.pickle', 'rb'))
y_train = pickle.load(open(output_dir + 'Y_train_l.pickle', 'rb'))
x_test = pickle.load(open(output_dir + 'X_test.pickle', 'rb'))
y_test = pickle.load(open(output_dir + 'Y_test.pickle', 'rb'))
img_rows, img_cols = x_train.shape[1], x_train.shape[2]
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')
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
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(32, kernel_size=(3, img_cols), activation='relu',
    input_shape=input_shape, strides=1))
model.add(Conv2D(64, (3, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.
    optimizers.Adadelta(), metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1,
    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])

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

batch_size = 32
num_classes = 2
epochs = 12

idx_fold = 0
col_p1 = 3
output_dir = 'data/fold_' + str(idx_fold) + '/col/' + str(col_p1-3) + '/'
x_train = pickle.load(open(output_dir + 'X_train_l.pickle', 'rb'))
y_train = pickle.load(open(output_dir + 'Y_train_l.pickle', 'rb'))

# print(y_train)
# x_train = x_train[:int(0.8*x_train.shape[0]),:,:]
# x_val = x_train[int(0.8*x_train.shape[0]):,:,:]
# y_train = y_train[:int(0.8*y_train.shape[0])]
# y_val = y_train[int(0.8*y_train.shape[0]):]
# print(y_val)
# x_test = pickle.load(open(output_dir + 'X_test.pickle', 'rb'))
# y_test = pickle.load(open(output_dir + 'Y_test.pickle', 'rb'))

x_test = pickle.load(open(output_dir + 'X_test.pickle', 'rb'))
y_test = pickle.load(open(output_dir + 'Y_test.pickle', 'rb'))
# print(y_test)

img_rows, img_cols = x_train.shape[1], x_train.shape[2]
# input image dimensions
# img_rows, img_cols = 28, 28

# the data, shuffled and split between train and test sets
# (x_train, y_train), (x_test, y_test) = 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')
# print(y_train[:100])

# 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(32, kernel_size=(3, img_cols),
                 activation='relu',
                 input_shape=input_shape,
                 strides=1))
model.add(Conv2D(64, (3, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 1)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

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

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          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])