transfer learning with inception v3

dog_breed_transfer.learning.py

bottleneck_features = np.load('inception_v3_file.npz')
train_inception_v3 = bottleneck_features['train']
valid_inception_v3 = bottleneck_features['valid']
test_inception_v3 = bottleneck_features['test']


model = Sequential()
model.add(Dense(input_shape=train_inception_v3.shape[1:]), units=16))
model.add(Conv2D(filters = 16, kernel_size=2, strides=1, padding ='valid', activation='relu'))
model.add(MaxPooling2D(pool_size = (2), strides=(2), padding ='valid'))
model.add(Conv2D(filters = 32, kernel_size=2, strides=1, padding ='valid', activation='relu'))
model.add(MaxPooling2D(pool_size = (2), strides=(2), padding ='valid'))
model.add(Conv2D(filters = 64, kernel_size=2, strides=1, padding ='valid', activation='relu'))
model.add(MaxPooling2D(pool_size = (2), strides=(2), padding ='same'))
model.add(GlobalAveragePooling2D())
model.add(Dense(units=133, activation='softmax'))