-> If using theano backend:
THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32
import numpy as np
import datetime
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
from numpy import nan
now = datetime.datetime.now
Using TensorFlow backend.
now = datetime.datetime.now
batch_size = 128
nb_classes = 5
nb_epoch = 5
# input image dimensions
img_rows, img_cols = 28, 28
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
pool_size = 2
# convolution kernel size
kernel_size = 3
if K.image_data_format() == 'channels_first':
input_shape = (1, img_rows, img_cols)
else:
input_shape = (img_rows, img_cols, 1)
def train_model(model, train, test, nb_classes):
X_train = train[0].reshape((train[0].shape[0],) + input_shape)
X_test = test[0].reshape((test[0].shape[0],) + input_shape)
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 = np_utils.to_categorical(train[1], nb_classes)
Y_test = np_utils.to_categorical(test[1], nb_classes)
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
t = now()
model.fit(X_train, Y_train,
batch_size=batch_size, nb_epoch=nb_epoch,
verbose=1,
validation_data=(X_test, Y_test))
print('Training time: %s' % (now() - t))
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# create two datasets one with digits below 5 and one with 5 and above
X_train_lt5 = X_train[y_train < 5]
y_train_lt5 = y_train[y_train < 5]
X_test_lt5 = X_test[y_test < 5]
y_test_lt5 = y_test[y_test < 5]
X_train_gte5 = X_train[y_train >= 5]
y_train_gte5 = y_train[y_train >= 5] - 5 # make classes start at 0 for
X_test_gte5 = X_test[y_test >= 5] # np_utils.to_categorical
y_test_gte5 = y_test[y_test >= 5] - 5
# define two groups of layers: feature (convolutions) and classification (dense)
feature_layers = [
Convolution2D(nb_filters, kernel_size, kernel_size,
border_mode='valid',
input_shape=input_shape),
Activation('relu'),
Convolution2D(nb_filters, kernel_size, kernel_size),
Activation('relu'),
MaxPooling2D(pool_size=(pool_size, pool_size)),
Dropout(0.25),
Flatten(),
]
classification_layers = [
Dense(128),
Activation('relu'),
Dropout(0.5),
Dense(nb_classes),
Activation('softmax')
]
# create complete model
model = Sequential(feature_layers + classification_layers)
# train model for 5-digit classification [0..4]
train_model(model,
(X_train_lt5, y_train_lt5),
(X_test_lt5, y_test_lt5), nb_classes)
X_train shape: (30596, 1, 28, 28) 30596 train samples 5139 test samples Train on 30596 samples, validate on 5139 samples Epoch 1/5 30596/30596 [==============================] - 3s - loss: 0.2071 - acc: 0.9362 - val_loss: 0.0476 - val_acc: 0.9848 Epoch 2/5 30596/30596 [==============================] - 3s - loss: 0.0787 - acc: 0.9774 - val_loss: 0.0370 - val_acc: 0.9879 Epoch 3/5 30596/30596 [==============================] - 3s - loss: 0.0528 - acc: 0.9846 - val_loss: 0.0195 - val_acc: 0.9926 Epoch 4/5 30596/30596 [==============================] - 3s - loss: 0.0409 - acc: 0.9880 - val_loss: 0.0152 - val_acc: 0.9942 Epoch 5/5 30596/30596 [==============================] - 3s - loss: 0.0336 - acc: 0.9901 - val_loss: 0.0135 - val_acc: 0.9959 Training time: 0:00:40.094398 Test score: 0.0135238260214 Test accuracy: 0.995913601868
# freeze feature layers and rebuild model
for l in feature_layers:
l.trainable = False
# transfer: train dense layers for new classification task [5..9]
train_model(model,
(X_train_gte5, y_train_gte5),
(X_test_gte5, y_test_gte5), nb_classes)
X_train shape: (29404, 1, 28, 28) 29404 train samples 4861 test samples Train on 29404 samples, validate on 4861 samples Epoch 1/5 29404/29404 [==============================] - 1s - loss: 0.3810 - acc: 0.8846 - val_loss: 0.0897 - val_acc: 0.9728 Epoch 2/5 29404/29404 [==============================] - 1s - loss: 0.1245 - acc: 0.9607 - val_loss: 0.0596 - val_acc: 0.9825 Epoch 3/5 29404/29404 [==============================] - 1s - loss: 0.0927 - acc: 0.9714 - val_loss: 0.0467 - val_acc: 0.9860 Epoch 4/5 29404/29404 [==============================] - 1s - loss: 0.0798 - acc: 0.9755 - val_loss: 0.0408 - val_acc: 0.9868 Epoch 5/5 29404/29404 [==============================] - 1s - loss: 0.0704 - acc: 0.9783 - val_loss: 0.0353 - val_acc: 0.9887 Training time: 0:00:07.964140 Test score: 0.0352752654647 Test accuracy: 0.988685455557
Try to Fine Tune a VGG16 Network
## your code here
...
...
# Plugging new Layers
model.add(Dense(768, activation='sigmoid'))
model.add(Dropout(0.0))
model.add(Dense(768, activation='sigmoid'))
model.add(Dropout(0.0))
model.add(Dense(n_labels, activation='softmax'))