print(np.shape(x_train_resized))
print(np.shape(x_test_resized))
model = MobileNet(include_top=True,
weights=None,
classes=2,
pooling='max',
input_shape=(200, 200, 3))
model.load_weights(weight_path)
checkpoint = ModelCheckpoint(filepath=os.path.join(
save_dir,
'MobileNetV2_weight.{epoch:02d}-{loss:.2f}-{categorical_accuracy:.2f}.hdf5'
),
verbose=1,
monitor='categorical_accuracy',
save_best_only=True)
opt = Adam(lr=5e-6)
model.compile(optimizer=opt,
loss=losses.categorical_crossentropy,
metrics=[metrics.categorical_accuracy])
# model.fit(x_train_resized,y_train,epochs=20,batch_size=6,callbacks=[checkpoint])
#
# model.save(model_path)
# model.save_weights(weight_path)
score1 = model.evaluate(x_train_resized, y_train, batch_size=6)
score2 = model.evaluate(x_test_resized, y_test, batch_size=6)
print(score1)
print(score2)
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
# Compute quantities required for feature-wise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(x_train)
# Fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(x_train, y_train,
batch_size=batch_size),
epochs=epochs,
validation_data=(x_test, y_test),
workers=4)
# Save model and weights
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)
# Score trained model.
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
