def train(num_classes=100, epochs=100, reps=1):
(x_train, y_train) = load_data(num_classes,reps)
model = Sequential()
#model.add(GaussianNoise(0.95, input_shape=(784,)))
model.add(Lambda(lambda x: x+K.random_normal(shape=K.shape(x), mean=0., stddev=0.10), input_shape=(784,)))#permanent noise
#model.add(Dropout(0.95, input_shape=(784,))) #regular dropout
model.add(Lambda(lambda x: K.dropout(x, level=0.9), input_shape=(784,)))#permanent dropout
model.add(Dense(1024, activation='relu', input_shape=(784,)))
model.add(Dense(1024, activation='relu', input_shape=(784,)))
model.add(Dense(num_classes, activation='softmax'))
model.load_weights("saved_noise.hdf5")
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
#add callbacks
tensorboard = TensorBoard(log_dir="logs/{}".format(time()), histogram_freq=100, write_graph=True, write_images=False)
checkpoint = ModelCheckpoint("saved_noise.hdf5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=10)
stopper = EarlyStopping(monitor='val_acc', min_delta=0.01, patience=100, verbose=1, mode='auto')
model.fit(x_train, y_train,
batch_size=num_classes*reps,
epochs=epochs,
verbose=1,
validation_data=(x_train,y_train),
callbacks=[tensorboard, checkpoint, stopper])
return model
# find indexes of all samples with label "7"
idx = (y_train == 7).nonzero()[0]
idx = idx[0:num_classes] #keep first 1000 examples
# remove all other examples
x_train = x_train[idx, :]
# create new y_train
y_train = list(range(0, num_classes))
y_train = keras.utils.to_categorical(y_train, num_classes)
model = Sequential()
#model.add(Dropout(0.95, input_shape=(784,))) #regular dropout
model.add(Lambda(lambda x: K.dropout(x, level=0.9),
input_shape=(784, ))) #permanent dropout
#model.add(GaussianNoise(0.95, input_shape=(784,)))
model.add(Dense(1024, activation='relu', input_shape=(784, )))
model.add(Dense(1024, activation='relu', input_shape=(784, )))
model.add(Dense(num_classes, activation='softmax'))
model.load_weights("saved.hdf5")
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
#add callbacks
tensorboard = TensorBoard(log_dir="logs/{}".format(time()),
