def compute_embeddings_pretrained(parameters, train_X, val_X, test=None):
if parameters['cnn'] == 'vgg':
cnn_model = vgg19.VGG19(weights='imagenet', pooling='avg')
cnn = Model(inputs=cnn_model.input,
outputs=cnn_model.get_layer('fc2').output)
if parameters['cnn'] == 'resnet':
#cnn = resnet50.ResNet50(weights='imagenet',include_top=False,pooling=parameters['pool'])
#cnn = Model(inputs=cnn_model.input, outputs=cnn_model.get_layer('').output)
cnn = resnet.ResNet101(include_top=False,
weights='imagenet',
pooling='avg')
if parameters['cnn'] == 'inc_resnet':
cnn = inception_resnet_v2.InceptionResNetV2(weights='imagenet',
include_top=False,
pooling=parameters['pool'])
#cnn = Model(inputs=cnn_model.input, outputs=cnn_model.get_layer('').output)
if parameters['cnn'] == 'densenet':
cnn = densenet.DenseNet121(weights='imagenet',
include_top=False,
pooling=parameters['pool'])
if parameters['cnn'] == 'xception':
cnn = xception.Xception(weights='imagenet',
include_top=False,
pooling=parameters['pool'])
'''
if parameters['freeze']:
for layer in cnn.layers:
layer.trainable = False
print ('TRAINABLE LAYERS: ')
for layer in cnn.layers:
print(layer, layer.trainable)
'''
embeddings_train = cnn.predict(train_X)
embeddings_val = cnn.predict(val_X)
print('Dim embeddings train: %s', str(embeddings_train.shape))
print('Dim embeddings val: %s', str(embeddings_val.shape))
if test is not None:
embeddings_test = {}
for k, v in test.items():
if parameters['cnn'] == 'resnet':
embeddings_test[k] = cnn.predict(v) #.reshape((2048,-1))
elif parameters['cnn'] == 'inc_resnet':
embeddings_test[k] = cnn.predict(v) #.reshape((1536,-1))
elif parameters['cnn'] == 'xception':
embeddings_test[k] = cnn.predict(v) #.reshape((,-1))
else:
embeddings_test[k] = cnn.predict(v) #.reshape((1024,-1))
with open(parameters['embeddings_pretrained_test'], 'wb') as f:
pickle.dump(embeddings_test, f, protocol=pickle.HIGHEST_PROTOCOL)
print('Dim embeddings test: %s', str(len(embeddings_test)))
return embeddings_train.squeeze(), embeddings_val.squeeze()
input_shape=img_dims)
elif model_name in ['vgg16', 'VGG16']:
model = vgg16.VGG16(include_top=False,
weights='imagenet',
input_shape=img_dims)
elif model_name in ['vgg19', 'VGG19']:
model = vgg19.VGG19(include_top=False,
weights='imagenet',
input_shape=img_dims)
elif model_name in ['resnet50', 'ResNet50']:
model = ResNet50(include_top=False,
weights='imagenet',
input_shape=img_dims)
elif model_name in ['resnet101', 'ResNet101']:
model = resnet.ResNet101(include_top=False,
weights='imagenet',
input_shape=img_dims)
# print(model.summary())
#fine tuning
output = model.layers[-1].output
output = keras.layers.Flatten()(output)
i = 0
new_model = Model(model.input, output=output)
for layer in new_model.layers:
layer.trainable = True
model = Sequential()
model.add(new_model)
model.add(Dense(img_dims[0], activation='relu', input_dim=img_dims))
model.add(Dropout(0.3))