def predict(image):
model = NASNetMobile()
pred = model.predict(image)
decoded_predictions = decode_predictions(pred, top=10)
response = 'NASNetMobile predictions: ' + str(
decoded_predictions[0][0:5])
print(response)
np.argmax(pred[0])
return response
import keras
from keras.applications.nasnet import NASNetMobile
from keras.preprocessing import image
from keras.applications.xception import preprocess_input, decode_predictions
import numpy as np
import tensorflow as tf
from keras import backend as K
sess = tf.Session()
K.set_session(sess)
model = NASNetMobile(weights="imagenet")
img = image.load_img('cat.jpg', target_size=(224, 224))
img_arr = np.expand_dims(image.img_to_array(img), axis=0)
x = preprocess_input(img_arr)
preds = model.predict(x)
print('Prediction:', decode_predictions(preds, top=5)[0])
# save the model for use with TensorFlow
builder = tf.saved_model.builder.SavedModelBuilder("nasnet")
# Tag the model, required for Go
builder.add_meta_graph_and_variables(sess, ["atag"])
builder.save()
sess.close()
class FeatureExtractor:
def __init__(self, net='VGG16', weights='imagenet', size=None):
print('[+] initialize: {}, weights: {}'.format(net, weights))
if net == 'Xception':
from keras.applications.xception import Xception
from keras.applications.xception import preprocess_input
self.model = Xception(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == 'VGG16':
print('[+] initialize using VGG16...')
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
self.model = VGG16(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == 'VGG19':
from keras.applications.vgg19 import VGG19
from keras.applications.vgg19 import preprocess_input
self.model = VGG19(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == 'ResNet50':
from keras.applications.resnet50 import ResNet50
from keras.applications.resnet50 import preprocess_input
self.model = ResNet50(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == 'InceptionV3':
from keras.applications.inception_v3 import InceptionV3
from keras.applications.inception_v3 import preprocess_input
self.model = InceptionV3(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (299, 299)
elif net == 'InceptionResNetV2':
from keras.applications.inception_resnet_v2 import InceptionResNetV2
from keras.applications.inception_resnet_v2 import preprocess_input
self.model = InceptionResNetV2(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (299, 299)
elif net == 'DenseNet121':
from keras.applications.densenet import DenseNet121
from keras.applications.densenet import preprocess_input
self.model = DenseNet121(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == 'DenseNet169':
from keras.applications.densenet import DenseNet169
from keras.applications.densenet import preprocess_input
self.model = DenseNet169(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == 'DenseNet201':
from keras.applications.densenet import DenseNet201
from keras.applications.densenet import preprocess_input
self.model = DenseNet201(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == 'NASNetLarge':
from keras.applications.nasnet import NASNetLarge
from keras.applications.nasnet import preprocess_input
self.model = NASNetLarge(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == 'NASNetMobile':
from keras.applications.nasnet import NASNetMobile
from keras.applications.nasnet import preprocess_input
self.model = NASNetMobile(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
else:
print("FeatureExtractor: Net not found")
self.preprocess_input = preprocess_input
def extract(self, fp_im):
#im_np = ensure_np(im)
#im = cv.resize(im_np, self.input_size) # force resize
#im = cv.cvtColor(im, cv.COLOR_RGBA2BGR)
im = keras_image.load_img(fp_im,
target_size=(224, 224)) # convert np.ndarray
x = keras_image.img_to_array(im) # reshape to (3, 224, 224)
x = x.copy(order="C")
#im_rgb = Image.open(fp_im)
#im_np_rgb = ensure_np(im_rgb)
#im_np_bgr = cv.cvtColor(im_np_rgb, cv.COLOR_RGB2BGR)
#x = keras_image.img_to_array(im_np_bgr) # reshape to (3, 224, 224)
x = np.expand_dims(x, axis=0) # expand to (1, 3, 224, 224)
#from keras.applications.vgg16 import preprocess_input as keras_preprocess
x = self.preprocess_input(x)
print(x.shape)
feats = self.model.predict(x)[0] # extract features
#feats_arr = np.char.mod('%f', features) # convert to list
feats_norm = feats / np.linalg.norm(feats)
return feats_norm
# Load our model
# model = densenet169_model(img_rows=img_rows, img_cols=img_cols, color_type=channel, num_classes=num_classes)
# load keras model
model = NASNetMobile(weights=None, classes=10)
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# Start Fine-tuning
model.fit(
X_train,
Y_train,
batch_size=batch_size,
epochs=nb_epoch,
shuffle=True,
verbose=1,
validation_data=(X_valid, Y_valid),
)
# Make predictions
predictions_valid = model.predict(X_valid,
batch_size=batch_size,
verbose=1)
# Cross-entropy loss score
score = log_loss(Y_valid, predictions_valid)
data_ori = np.concatenate((data_cat, data_dog), axis=0)
data_ori1 = np.concatenate((data_cat1, data_dog1), axis=0)
# print(data_ori.shape)
data_X = np.zeros(7 * 7 * 2048)
for item in data_ori:
data1 = model_Res.predict(item)
# print(data1.shape)
data1 = data1.reshape(1, 7 * 7 * 2048)
data_X = np.vstack((data_X, data1))
data_X = data_X[1:, :]
# print(data_X.shape)
data_Y = np.hstack((np.zeros(200), np.ones(200)))
data_X1 = np.zeros(7 * 7 * 1056)
for item in data_ori1:
data3 = model_NasMobile.predict(item)
# print(data3.shape)
data3 = data3.reshape(1, 7 * 7 * 1056)
data_X1 = np.vstack((data_X1, data3))
data_X1 = data_X1[1:, :]
# print(data_X.shape)
data_Y1 = np.hstack((np.zeros(200), np.ones(200)))
from keras.models import Sequential
from keras.layers import Dense, Activation
model_after_Res = Sequential([
Dense(10, input_dim=100352),
Activation('relu'),
Dense(1),
Activation('sigmoid')
class FeatureExtractor:
def __init__(self,
weights='imagenet',
net=KerasNet.VGG16,
gpu_ram=0.3,
size=None):
"""Initialize Keras feature extractor
:param weights: (str) name of the weights network
:param net: (KerasNet) network type
"""
from keras.preprocessing import image as keras_image
self.keras_image = keras_image
if net == KerasNet.XCEPTION:
from keras.applications.xception import Xception
from keras.applications.xception import preprocess_input
self.model = Xception(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == KerasNet.VGG16:
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
self.model = VGG16(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == KerasNet.VGG19:
from keras.applications.vgg19 import VGG19
from keras.applications.vgg19 import preprocess_input
self.model = VGG19(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == KerasNet.RESNET50:
from keras.applications.resnet50 import ResNet50
from keras.applications.resnet50 import preprocess_input
self.model = ResNet50(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == KerasNet.INCEPTIONV2:
from keras.applications.inception_resnet_v2 import InceptionResNetV2
from keras.applications.inception_resnet_v2 import preprocess_input
self.model = InceptionResNetV2(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (299, 299)
elif net == KerasNet.INCEPTIONV3:
from keras.applications.inception_v3 import InceptionV3
from keras.applications.inception_v3 import preprocess_input
self.model = InceptionV3(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (299, 299)
elif net == KerasNet.DENSENET121:
from keras.applications.densenet import DenseNet121
from keras.applications.densenet import preprocess_input
self.model = DenseNet121(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == KerasNet.DENSENET160:
from keras.applications.densenet import DenseNet169
from keras.applications.densenet import preprocess_input
self.model = DenseNet169(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == KerasNet.DENSENET201:
from keras.applications.densenet import DenseNet201
from keras.applications.densenet import preprocess_input
self.model = DenseNet201(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == KerasNet.NASNETLARGE:
from keras.applications.nasnet import NASNetLarge
from keras.applications.nasnet import preprocess_input
self.model = NASNetLarge(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
elif net == KerasNet.NASNETMOBILE:
from keras.applications.nasnet import NASNetMobile
from keras.applications.nasnet import preprocess_input
self.model = NASNetMobile(weights=weights,
include_top=False,
pooling='avg')
self.input_size = size if size is not None else (224, 224)
self.preprocess_input = preprocess_input
def extract_fp(self, fp_im, normalize=True):
"""Loads image and generates"""
im = cv.imread(fp_im)
return self.extract(im, normalize=normalize)
def extract(self, im, normalize=True):
"""Get vector embedding from Numpy image array
:param im: Numpy.ndarray Image
:param normalize: If True, returns normalized feature vector
:returns: Numpy ndarray
"""
# im = im_utils.ensure_np(im)
im = cv.resize(im, self.input_size) # force resize
#im = keras_image.load_img(fp_im, target_size=(224, 224)) # convert np.ndarray
x = self.keras_image.img_to_array(im) # reshape to (3, 224, 224)
x = np.expand_dims(x, axis=0) # expand to (1, 3, 224, 224)
x = self.preprocess_input(x)
feats = self.model.predict(x)[0] # extract features
#feats_arr = np.char.mod('%f', features) # convert to list
if normalize:
feats = feats / np.linalg.norm(feats)
return feats