def save_bioimaging_bottleneck(glob_variables):
model = ResNet152V2(include_top=False, weights='imagenet')
datagen = ImageDataGenerator(rescale=1. / 255)
generator = datagen.flow_from_directory(
glob_variables['bioimaging_patch_images_data_dir'] +
str(glob_variables['img_width']),
target_size=(glob_variables['img_width'],
glob_variables['img_height']),
batch_size=glob_variables['batch_size'],
class_mode=None,
shuffle=False)
nb_validation_samples = len(generator.filenames)
predict_size_validation = int(
math.ceil(nb_validation_samples / glob_variables['batch_size']))
bottleneck_features_validation = model.predict(generator,
predict_size_validation)
np.save(
'bioimaging/bottleneck_features_validation_bioimaging_' +
glob_variables['model'] + '_' + str(glob_variables['img_width']) +
'.npy', bottleneck_features_validation)
def evaluation(args):
path_img_val = '../datasets/ilsvrc2012/images/val/'
path_val_info = '../datasets/ilsvrc2012/images/val.txt'
if args.model == 'vgg16':
model = VGG16(weights='imagenet')
model.summary()
elif args.model == 'resnet152':
model = ResNet152(weights='imagenet')
model.summary()
elif args.model == 'resnet152v2':
model = ResNet152V2(weights='imagenet')
model.summary()
elif args.model == 'inceptionresnetv2':
model = InceptionResNetV2(weights='imagenet')
model.summary()
elif args.model == 'densenet201':
model = DenseNet201(weights='imagenet')
model.summary()
elif args.model == 'nasnetlarge':
model = NASNetLarge(weights='imagenet')
model.summary()
name, label = load_header_imagenet(load_file(path_val_info))
pred = list()
for i, n in enumerate(name):
x = preprocessing_imagenet(path_img_val + n, args)
pred.append(np.argmax(model.predict(x), axis=1)[0])
if i % 1000 == 0:
print(n)
correct = len([p for p, l in zip(pred, label) if p == l])
print('Accuracy of the IMAGENET dataset using model %s: %.4f' %
(args.model, correct / len(label)))
def apply_Feature_Extractor_model(params):
"""
Apply a previously trained model.
:param params: Hyperparameters
:return:
"""
model = None
if params['MODEL_TYPE'] == 'InceptionV3':
model = InceptionV3(weights='imagenet', include_top=False)
elif params['MODEL_TYPE'] == 'NASNetLarge':
model = NASNetLarge(weights='imagenet', include_top=False)
elif params['MODEL_TYPE'] == 'ResNet152':
model = ResNet152V2(weights='imagenet', include_top=False)
print(model.summary())
base_path = params['DATA_ROOT_PATH']
for s in params['EXTRACT_ON_SETS']:
if params['SPLIT_OUTPUT']:
path_general = params['STORE_PATH'] + '/' + params.get(
'MODEL_TYPE', 'features') + '/' + s + '/'
if not os.path.isdir(
path_general): # create dir if it doesn't exist
os.makedirs(path_general)
list_filepath = base_path + '/' + params['IMG_FILES'][s]
image_list = file2list(list_filepath)
eta = -1
start_time = time.time()
n_images = len(image_list)
for n_sample, imname in list(enumerate(image_list)):
if params['MODEL_TYPE'] == 'InceptionV3':
features = inceptionV3(model, imname)
elif params['MODEL_TYPE'] == 'NASNetLarge':
features = nasNetLarge(model, imname)
elif params['MODEL_TYPE'] == 'ResNet152':
features = resNet152(model, imname)
# Keras puts the spatial dimensions at the start. We may want to put them at the end
if params.get('SPATIAL_LAST', True):
features = features.transpose(0, 3, 1, 2)
filepath = path_general + imname.split(
'/')[-1][:-4] + '.npy' if imname.split(
'/')[-1][-4:] == '.jpg' or imname.split('/')[-1][
-4:] == '.png' else path_general + imname.split(
'/')[-1] + '.npy'
numpy2file(filepath, features, permission='wb', split=False)
sys.stdout.write('\r')
sys.stdout.write("\t Processed %d/%d - ETA: %ds " %
(n_sample, n_images, int(eta)))
sys.stdout.flush()
eta = (n_images - n_sample) * (time.time() - start_time) / max(
n_sample, 1)
print("Features saved in", path_general)
def load_models(self):
"""
Instantiates models and loads the weights.
**MUST LOAD BEFORE RUNNING MODELS**
"""
input_tensor = Input(shape=(self.dim_size, self.dim_size, 3))
self.resnet = ResNet152V2(input_tensor=input_tensor, weights=None, classes=7)
self.resnet.load_weights('weights_resnet224.h5', by_name=False)
self.inception = InceptionV3(input_tensor=input_tensor, weights=None, classes=7)
self.inception.load_weights('weights_inception224.h5', by_name=False)
self.inceptionresnet = InceptionResNetV2(input_tensor=input_tensor, weights=None, classes=7)
self.inceptionresnet.load_weights('weights_inceptionresnet224.h5', by_name=False)
def output_make(input_path, label_path, target):
with tf.device('/cpu:0'):
base_model = ResNet152V2(weights=None,
include_top=False,
input_shape=(224, 224, 1))
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(0.3)(x)
predictions = Dense(5, activation='softmax')(x)
resnet_model = Model(inputs=base_model.input, outputs=predictions)
# resnet_hist = compile_and_train(resnet_model, num_epochs)
resnet_model.load_weights('./PredictHouseholdIncome_pradeep4444.hdf5')
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
resnet_model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
X = make_img(input_path)
X = normalize_X(X)
pickle_path = os.path.join(input_path, 'processed_pickles')
make_pickle(X, 'pickled_image_array', pickle_path)
X = read_pickle('pickled_image_array', pickle_path)
y_preds = resnet_model.predict(X)
try:
y_OHE = make_label_array(label_path, target)
y_preds = resnet_aug.predict(X_test)
matrix = confusion_matrix(y_OHE.argmax(axis=1),
y_preds.argmax(axis=1))
y_pred_bool = np.argmax(y_preds, axis=1)
y_test_bool = np.argmax(y_OHE, axis=1)
class_report = classification_report(y_test_bool, y_pred_bool)
with open('output.txt', 'w') as op_handle:
op_handle.write('raw predictions:\n')
op_handle.write(str(y_preds))
op_handle.write('\n')
op_handle.write('confusion martix:\n')
op_handle.write(str(matrix))
op_handle.write('\n')
op_handle.write('classification report:\n')
op_handle.write(str(class_report))
op_handle.write('\n')
except:
pass
def load_models(self):
input_tensor = Input(shape=(self.dim_size, self.dim_size, 3))
self.resnet = ResNet152V2(input_tensor=input_tensor,
weights=None,
classes=7)
self.resnet.load_weights('weights_resnet224.h5', by_name=False)
self.inception = InceptionV3(input_tensor=input_tensor,
weights=None,
classes=7)
self.inception.load_weights('weights_inception224.h5', by_name=False)
self.inceptionresnet = InceptionResNetV2(input_tensor=input_tensor,
weights=None,
classes=7)
self.inceptionresnet.load_weights('weights_inceptionresnet224.h5',
by_name=False)
def ResNet152V2model(no_classes, shape):
"""
Deep CNN 50 layers
"""
base_model = ResNet152V2(include_top=False,
weights='imagenet',
input_shape=shape)
# Freeze the base_model
base_model.trainable = False
inputs = Input(shape=shape)
x = base_model(inputs, training=False)
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu', name='predictions')(x)
#x = Dropout(0.2)(x)
predictions = Dense(no_classes, activation='softmax')(x)
model = Model(inputs, outputs=predictions)
return model
def predict_single(glob_variables):
model = None
if glob_variables['model'] == 'Xception':
model = Xception(include_top=False, weights='imagenet')
elif glob_variables['model'] == 'NASNetLarge':
model = NASNetLarge(include_top=False, weights='imagenet')
elif glob_variables['model'] == 'DenseNet201':
model = DenseNet201(include_top=False, weights='imagenet')
elif glob_variables['model'] == 'VGG16':
model = VGG16(include_top=False, weights='imagenet')
elif glob_variables['model'] == 'VGG19':
model = VGG19(include_top=False, weights='imagenet')
elif glob_variables['model'] == 'InceptionV3':
model = InceptionV3(include_top=False, weights='imagenet')
elif glob_variables['model'] == 'InceptionResNetV2':
model = InceptionResNetV2(include_top=False, weights='imagenet')
elif glob_variables['model'] == 'ResNet152V2':
model = ResNet152V2(include_top=False, weights='imagenet')
datagen = ImageDataGenerator(rescale=1. / 255)
generator = datagen.flow_from_directory(
'predict_single/',
target_size=(glob_variables['img_width'],
glob_variables['img_height']),
batch_size=glob_variables['batch_size'],
class_mode=None,
shuffle=False)
nb_validation_samples = len(generator.filenames)
predict_size_validation = int(
math.ceil(nb_validation_samples / glob_variables['batch_size']))
bottleneck_features_validation = model.predict(generator,
predict_size_validation)
return bottleneck_features_validation
def load_model(dim_size):
input_tensor = Input(shape=(dim_size, dim_size, 3))
global model
model = ResNet152V2(input_tensor=input_tensor, weights=None, classes=7)
if path.exists(weights):
model.load_weights(weights, by_name=False)
#for layer in model.layers[:551]:
# layer.trainable = False
#for layer in model.layers[551:]:
# layer.trainable = True
for layer in model.layers:
layer.trainable = True
model.compile(optimizer=SGD(lr=0.01, momentum=0.9),
loss='categorical_crossentropy',
metrics=["accuracy"])
search = OptimizationSearch(
np.load('data/vgg19_%s_correct.npy' % SAMPLE, allow_pickle=True),
filter,
model,
vgg19_preprocess_input
)
results, scores = search.perform_search(iterations=ITERATIONS)
save_filter_search_scores(filter, results, scores, 'log/fourier_uniform_filter_vgg19_%s_search.csv' % SAMPLE)
if NETWORK == 'densenet':
model = DenseNet201(weights='imagenet')
search = OptimizationSearch(
np.load('data/densenet201_%s_correct.npy' % SAMPLE, allow_pickle=True),
filter,
model,
densenet_preprocess_input
)
results, scores = search.perform_search(iterations=ITERATIONS)
save_filter_search_scores(filter, results, scores, 'log/fourier_uniform_filter_densenet201_%s_search.csv' % SAMPLE)
if NETWORK == 'resnet':
model = ResNet152V2(weights='imagenet')
search = OptimizationSearch(
np.load('data/resnet152v2_%s_correct.npy' % SAMPLE, allow_pickle=True),
filter,
model,
resnet_preprocess_input
)
results, scores = search.perform_search(iterations=ITERATIONS)
save_filter_search_scores(filter, results, scores, 'log/fourier_uniform_filter_resnet152v2_%s_search.csv' % SAMPLE)
from keras.applications.resnet_v2 import ResNet152V2
from keras.preprocessing import image
from keras.applications.resnet_v2 import preprocess_input
from keras.layers import Input
from util import map_scores
import numpy as np
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
tf.keras.backend.set_session(tf.Session(config=config))
dim_size = 112
input_tensor = Input(shape=(dim_size, dim_size, 3))
model = ResNet152V2(input_tensor=input_tensor, weights=None, classes=7)
model.load_weights('resnet_weights.h5', by_name=False)
img_path = 'test_imgs/keratosis2.jpg'
img = image.load_img(img_path, target_size=(dim_size, dim_size))
x = image.img_to_array(img)
x = x / 255.0
x = np.expand_dims(x, axis=0)
#x = preprocess_input(x)
preds = model.predict(x)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', map_scores(preds, 3)[0])
def chosen_model(choice):
global base_model
if choice == 19:
model_exist()
else:
while (1):
print()
print(
'Transfer Learning? - Will use pre-trained model with imagenet weights'
)
print('y')
print('n')
weights_wanted = input()
if weights_wanted.upper() != 'Y' and weights_wanted.upper() != 'N':
print('ERROR: Please enter a valid choice')
else:
break
if choice == 1:
print('Selected Model = Xception')
if weights_wanted.upper() == 'Y':
base_model = Xception(weights='imagenet', include_top=False)
else:
base_model = Xception(weights=None, include_top=False)
if choice == 2:
print('Selected Model = VGG16')
if weights_wanted.upper() == 'Y':
base_model = VGG16(weights='imagenet', include_top=False)
else:
base_model = VGG16(weights=None, include_top=False)
if choice == 3:
print('Selected Model = VGG19')
if weights_wanted.upper() == 'Y':
base_model = VGG19(weights='imagenet', include_top=False)
else:
base_model = VGG19(weights=None, include_top=False)
if choice == 4:
print('Selected Model = ResNet50')
if weights_wanted.upper() == 'Y':
base_model = ResNet50(weights='imagenet', include_top=False)
else:
base_model = ResNet50(weights=None, include_top=False)
if choice == 5:
print('Selected Model = ResNet101')
if weights_wanted.upper() == 'Y':
base_model = ResNet101(weights='imagenet', include_top=False)
else:
base_model = ResNet101(weights=None, include_top=False)
if choice == 6:
print('Selected Model = ResNet152')
if weights_wanted.upper() == 'Y':
base_model = ResNet152(weights='imagenet', include_top=False)
else:
base_model = ResNet152(weights=None, include_top=False)
if choice == 7:
print('Selected Model = ResNet50V2')
if weights_wanted.upper() == 'Y':
base_model = ResNet50V2(weights='imagenet', include_top=False)
else:
base_model = ResNet50V2(weights=None, include_top=False)
if choice == 8:
print('Selected Model = ResNet101V2')
if weights_wanted.upper() == 'Y':
base_model = ResNet101V2(weights='imagenet', include_top=False)
else:
base_model = ResNet101V2(weights=None, include_top=False)
if choice == 9:
print('Selected Model = ResNet152V2')
if weights_wanted.upper() == 'Y':
base_model = ResNet152V2(weights='imagenet', include_top=False)
else:
base_model = ResNet152V2(weights=None, include_top=False)
if choice == 10:
print('Selected Model = InceptionV3')
if weights_wanted.upper() == 'Y':
base_model = InceptionV3(weights='imagenet', include_top=False)
else:
base_model = InceptionV3(weights=None, include_top=False)
if choice == 11:
print('Selected Model = InceptionResNetV2')
if weights_wanted.upper() == 'Y':
base_model = InceptionResNetV2(weights='imagenet',
include_top=False)
else:
base_model = InceptionResNetV2(weights=None, include_top=False)
if choice == 12:
print('Selected Model = MobileNet')
if weights_wanted.upper() == 'Y':
base_model = MobileNet(weights='imagenet', include_top=False)
else:
base_model = MobileNet(weights=None, include_top=False)
if choice == 13:
print('Selected Model = MobileNetV2')
if weights_wanted.upper() == 'Y':
base_model = MobileNetV2(weights='imagenet', include_top=False)
else:
base_model = MobileNetV2(weights=None, include_top=False)
if choice == 14:
print('Selected Model = DenseNet121')
if weights_wanted.upper() == 'Y':
base_model = DenseNet121(weights='imagenet', include_top=False)
else:
base_model = DenseNet121(weights=None, include_top=False)
if choice == 15:
print('Selected Model = DenseNet169')
if weights_wanted.upper() == 'Y':
base_model = DenseNet169(weights='imagenet', include_top=False)
else:
base_model = DenseNet169(weights=None, include_top=False)
if choice == 16:
print('Selected Model = DenseNet201')
if weights_wanted.upper() == 'Y':
base_model = DenseNet201(weights='imagenet', include_top=False)
else:
base_model = DenseNet201(weights=None, include_top=False)
if choice == 17:
print('Selected Model = NASNetLarge')
if weights_wanted.upper() == 'Y':
base_model = NASNetLarge(weights='imagenet', include_top=False)
else:
base_model = NASNetLarge(weights=None, include_top=False)
if choice == 18:
print('Selected Model = NASNetMobile')
if weights_wanted.upper() == 'Y':
base_model = NASNetMobile(weights='imagenet',
include_top=False)
else:
base_model = NASNetMobile(weights=None, include_top=False)
CLASSES = len(os.listdir('data/train'))
print('Number of Classes = {}'.format(CLASSES))
x = base_model.output
x = GlobalAveragePooling2D(name='avg_pool')(x)
x = Dropout(0.4)(x)
predictions = Dense(CLASSES, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
training(model)
input_shape = x_train[0].shape
# Define network
if extract_model == 'vgg16':
from keras.applications.vgg16 import VGG16
trained_model = VGG16(include_top=False, input_shape=input_shape)
elif extract_model == 'inceptionv3':
from keras.applications.inception_v3 import InceptionV3
trained_model = InceptionV3(include_top=False, input_shape=input_shape)
elif extract_model == 'xception':
from keras.applications.xception import Xception
trained_model = Xception(include_top=False, input_shape=input_shape)
elif extract_model == 'resnet152v2':
from keras.applications.resnet_v2 import ResNet152V2
trained_model = ResNet152V2(include_top=False, input_shape=input_shape)
elif extract_model == 'inception_resnetv2':
from keras.applications.inception_resnet_v2 import InceptionResNetV2
trained_model = InceptionResNetV2(include_top=False, input_shape=input_shape)
output = trained_model.layers[-1].output
output = GlobalMaxPooling2D()(output)
output = Dense(100, activation='relu')(output)
output = Dense(num_classes, activation='softmax')(output)
model = Model(input=trained_model.input, output=output)
model.summary()
model_path = Path('/dovilabfs/work/tommaria/gw/gravityspy/gpu/' + train_set + '/new/' + extract_model + '/' + opt_method + '/' + model_name)
weights_file = join(model_path, model_name + '.weights.best.hdf5')
if not os.path.exists(model_path):
os.makedirs(model_path)
lr_reduction=ReduceLROnPlateau(monitor='val_loss',patience=5,
verbose=2,factor=.8)
estopping=EarlyStopping(monitor='val_loss',patience=30,verbose=2)
history=vgg19model.fit(pvx_train1,y_train1,
validation_data=(pvx_valid1,y_valid1),
epochs=800,batch_size=128,verbose=2,
callbacks=[checkpointer,lr_reduction,estopping])
history_plot(history)
vgg19model.load_weights(fw)
vgg19model.evaluate(pvx_test1,y_test1)
from keras.applications.resnet_v2 import ResNet152V2,preprocess_input as preiRN
#vx_train1,vx_valid1,vx_test1=\
#preiRN(x_train1),preiRN(x_valid1),preiRN(x_test1)
RNbmodel=ResNet152V2(weights='imagenet',include_top=False)
pvx_train1=RNbmodel.predict(x_train1)
pvx_valid1=RNbmodel.predict(x_valid1)
pvx_test1=RNbmodel.predict(x_test1)
pvx_train1=pvx_train1.reshape(-1,1,1,pvx_train1.shape[3])
pvx_valid1=pvx_valid1.reshape(-1,1,1,pvx_valid1.shape[3])
pvx_test1=pvx_test1.reshape(-1,1,1,pvx_test1.shape[3])
sh=pvx_train1.shape[1:]
def RNmodel():
model=Sequential()
model.add(GlobalAveragePooling2D(input_shape=sh))
model.add(Dense(2048))
model.add(LeakyReLU(alpha=.02))
model.add(Dropout(.25))
model.add(Dense(512))
bottle_neck = model.predict(preprocess_input(res1),verbose=1)
prediction = int(np.argmax(model1.predict(bottle_neck),axis=1))
ax.set_title("I think it is "+dog_names[prediction])
Breed("test/test2.jpg")
"""# Using ResNet152V2"""
from keras.applications.resnet_v2 import ResNet152V2
from keras.applications.resnet_v2 import preprocess_input
from keras.preprocessing import image
from keras.layers import Input,Conv2D,MaxPooling2D,Dense,Dropout,BatchNormalization
from keras.models import Sequential
input_ = Input(shape=(224,224,3))
model_2 = ResNet152V2(include_top=False,weights='imagenet',input_tensor=input_)
model_2.summary()
#I have cleared the output, because the model was too+++ deep and you would have to scroll for some time .
def createdatasets_resnet(dataset):
data_set = np.zeros([dataset.shape[0],224,224,3])
for i,image_arr in enumerate(dataset):
img = cv2.imread(image_arr)
res = cv2.resize(img, dsize=(224, 224), interpolation=cv2.INTER_CUBIC)
data_set[i,:,:,:] = res
return data_set
bottle_neck_train = model_2.predict(preprocess_input(createdatasets_resnet(train_files)),verbose=1)
bottle_neck_test = model_2.predict(preprocess_input(createdatasets_resnet(test_files)),verbose=1)
def load_model(dim_size=224):
input_tensor = Input(shape=(dim_size, dim_size, 3))
model = ResNet152V2(input_tensor=input_tensor, weights=None, classes=7)
model.load_weights('resnet_weights320.h5', by_name=False)
return model
from keras.applications.resnet_v2 import ResNet152V2
from keras.preprocessing import image
from keras.applications.resnet_v2 import preprocess_input, decode_predictions
from keras.layers import Input
import numpy as np
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
tf.keras.backend.set_session(tf.Session(config=config))
dim_size = 224
input_tensor = Input(shape=(dim_size, dim_size, 3))
model = ResNet152V2(input_tensor=input_tensor, weights='imagenet')
#model = ResNet152V2(input_tensor=input_tensor, weights=None, classes=7)
#model.load_weights('resnet_weights.h5', by_name=False)
img_path = 'tiger.jpg'
img = image.load_img(img_path, target_size=(dim_size, dim_size))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', decode_predictions(preds, top=5))
# Predicted:
# [(u'n02504013', u'Indian_elephant', 0.82658225),
# (u'n01871265', u'tusker', 0.1122357),
plt.plot(history4.history['loss'])
plt.plot(history4.history['val_loss'])
plt.title('Inception Resnet model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.savefig('inception-resnet_loss')
plt.show()
"""## ResNet152V2"""
from keras.applications.resnet_v2 import ResNet152V2
resnet152 = ResNet152V2(include_top=False, weights='imagenet')
resnet152.summary()
x = resnet152.output
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.3)(x)
predictions = Dense(4, activation='softmax')(x)
model = Model(inputs=resnet152.input, outputs=predictions)
for layer in resnet152.layers:
layer.trainable = False
model.compile(loss='categorical_crossentropy',
def Build_Model_ResNet(px_train , pSessionParameters , pTrainingParameters ):
from keras.applications.resnet import ResNet50
from keras.applications.resnet import ResNet101
from keras.applications.resnet import ResNet152
from keras.applications.resnet_v2 import ResNet50V2
from keras.applications.resnet_v2 import ResNet101V2
from keras.applications.resnet_v2 import ResNet152V2
from keras.layers import Input
# Builds a new ResNet model
'''
Input parameters:
px_train: training data to be used to set the input shape of the model
pModelBuildParameters: Dict of Parameters to define how the model is built.
Return parameters: model
'''
BatchNormFlag = pSessionParameters['BatchNorm']
NoClasses = pSessionParameters['ModelBuildParameters']['NoClasses']
Activation = pSessionParameters['ModelBuildParameters']['Activation']
IncludeTopFlag = pSessionParameters['ModelBuildParameters']['IncludeTop']
Model = pSessionParameters['ModelBuildParameters']['Model']
Version = pSessionParameters['ModelBuildParameters']['Version']
if IncludeTopFlag:
if Version==1:
if 'ResNet50' in Model:
conv_base = ResNet50(input_shape=(px_train.shape[1:]), weights=None, include_top=True , classes = NoClasses, pooling='avg')
elif 'ResNet101' in Model:
conv_base = ResNet101(input_shape=(px_train.shape[1:]), weights=None, include_top=True , classes = NoClasses, pooling='avg')
elif 'ResNet152' in Model:
conv_base = ResNet152(input_shape=(px_train.shape[1:]), weights=None, include_top=True , classes = NoClasses, pooling='avg')
else: # Version 2
if 'ResNet50' in Model:
conv_base = ResNet50V2(input_shape=(px_train.shape[1:]), weights=None, include_top=True , classes = NoClasses, pooling='avg')
elif 'ResNet101' in Model:
conv_base = ResNet101V2(input_shape=(px_train.shape[1:]), weights=None, include_top=True , classes = NoClasses, pooling='avg')
elif 'ResNet152' in Model:
conv_base = ResNet152V2(input_shape=(px_train.shape[1:]), weights=None, include_top=True , classes = NoClasses, pooling='avg')
model = models.Sequential()
model.add(conv_base)
else:
if Version==1:
if 'ResNet50' in Model:
conv_base = ResNet50(input_shape=(px_train.shape[1:]), weights=None, include_top=False , classes = NoClasses, pooling='avg')
elif 'ResNet101' in Model:
conv_base = ResNet101(input_shape=(px_train.shape[1:]), weights=None, include_top=False , classes = NoClasses, pooling='avg')
elif 'ResNet152' in Model:
conv_base = ResNet152(input_shape=(px_train.shape[1:]), weights=None, include_top=False , classes = NoClasses, pooling='avg')
else: # Version 2
if 'ResNet50' in Model:
conv_base = ResNet50V2(input_shape=(px_train.shape[1:]), weights=None, include_top=False , classes = NoClasses, pooling='avg')
elif 'ResNet101' in Model:
conv_base = ResNet101V2(input_shape=(px_train.shape[1:]), weights=None, include_top=False , classes = NoClasses, pooling='avg')
elif 'ResNet152' in Model:
conv_base = ResNet152V2(input_shape=(px_train.shape[1:]), weights=None, include_top=False , classes = NoClasses, pooling='avg')
model = models.Sequential()
model.add(conv_base)
# Add Dense Layers and Dropout and BatchNorm layers based on ModelBuildParameters
model = Build_Model_AddLayers(model , pSessionParameters )
model.add(layers.Dense(NoClasses, activation=Activation, name='dense_class'))
return model