def test_mobilenet_variable_input_channels():
input_shape = (1, None, None) if K.image_data_format() == 'channels_first' else (None, None, 1)
model = applications.MobileNet(weights=None, include_top=False, input_shape=input_shape)
assert model.output_shape == (None, None, None, 1024)
input_shape = (4, None, None) if K.image_data_format() == 'channels_first' else (None, None, 4)
model = applications.MobileNet(weights=None, include_top=False, input_shape=input_shape)
assert model.output_shape == (None, None, None, 1024)
def test_mobilenet_image_size():
valid_image_sizes = [128, 160, 192, 224]
for size in valid_image_sizes:
input_shape = (size, size, 3) if K.image_data_format() == 'channels_last' else (3, size, size)
model = applications.MobileNet(input_shape=input_shape, weights='imagenet', include_top=True)
assert model.input_shape == (None,) + input_shape
invalid_image_shape = (112, 112, 3) if K.image_data_format() == 'channels_last' else (3, 112, 112)
with pytest.raises(ValueError):
model = applications.MobileNet(input_shape=invalid_image_shape, weights='imagenet', include_top=True)
def base_network(network='InceptionV3'):
if network == 'InceptionV3':
base_model = applications.InceptionV3(weights='imagenet',
include_top=False)
elif network == 'VGG16':
base_model = applications.VGG16(weights='imagenet', include_top=False)
elif network == 'VGG19':
base_model = applications.VGG19(weights='imagenet', include_top=False)
elif network == 'ResNet50':
base_model = applications.ResNet50(weights='imagenet',
include_top=False)
elif network == 'InceptionResNetV2':
base_model = applications.InceptionResNetV2(weights='imagenet',
include_top=False)
elif network == 'MobileNet':
base_model = applications.MobileNet(weights='imagenet',
include_top=False)
elif network == 'DenseNet121':
base_model = applications.DenseNet121(weights='imagenet',
include_top=False)
else:
print('Wrong Model selected.')
return None
return base_model
def test_04_plain_text_script(self):
model = applications.MobileNet(weights='imagenet',
include_top=False,
input_shape=(224, 224, 3))
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation='sigmoid')(x)
model_final = Model(inputs=model.input,
outputs=predictions,
name='predictions')
script_content = open("nyoka/tests/preprocess.py", 'r').read()
pmml_obj = KerasToPmml(model_final,
dataSet='image',
predictedClasses=['cat', 'dog'],
script_args={
"content": script_content,
"def_name": "getBase64EncodedString",
"return_type": "string",
"encode": False
})
pmml_obj.export(open("script_with_keras.pmml", 'w'), 0)
self.assertEqual(os.path.isfile("script_with_keras.pmml"), True)
reconPmmlObj = pml.parse("script_with_keras.pmml", True)
content = reconPmmlObj.TransformationDictionary.DefineFunction[
0].Apply.Extension[0].anytypeobjs_
content[0] = content[0].replace("\t", "")
content = "\n".join(content)
self.assertEqual(script_content, content)
self.assertEqual(len(model_final.layers),
len(reconPmmlObj.DeepNetwork[0].NetworkLayer))
def get_pretrained_model(model, img_size):
possible_names = ["vgg16", "vgg19", "mobilenet", "xception"]
if model not in possible_names:
raise ValueError(f"model needs to be either {possible_names}")
logger.debug(f"backend for model is {model}")
width, height = img_size
if K.image_data_format() == 'channels_first':
input_shape = (3, width, height)
else:
input_shape = (width, height, 3)
logger.debug(
f"backend suggests that we have the following input shape: {input_shape}"
)
models = {
"vgg16":
applications.VGG16(input_shape=input_shape,
include_top=False,
weights='imagenet'),
"vgg19":
applications.VGG19(input_shape=input_shape,
include_top=False,
weights='imagenet'),
"mobilenet":
applications.MobileNet(input_shape=input_shape,
include_top=False,
weights='imagenet'),
"xception":
applications.Xception(input_shape=input_shape,
include_top=False,
weights='imagenet')
}
return models[model]
def get_transfer_model():
# import inception with pre-trained weights. do not include fully #connected layers
if MODEL == "resnet50":
base_model = applications.ResNet50(weights='imagenet',
include_top=False)
elif MODEL == "mobilenet":
base_model = applications.MobileNet(weights='imagenet',
include_top=False,
input_shape=input_shape)
else:
raise Exception("Invalid model: '{}'".format(MODEL))
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# add a fully-connected layer
x = Dense(512, activation='relu')(x)
# and a fully connected output/classification layer
predictions = Dense(CLASS_COUNT, activation='softmax')(x)
# create the full network so we can train on it
transfer_learning_model = Model(inputs=base_model.input,
outputs=predictions)
# create the full network so we can train on it
transfer_learning_model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
return transfer_learning_model
def test_01_image_classifier_with_image_as_input(self):
model = applications.MobileNet(weights='imagenet',
include_top=False,
input_shape=(224, 224, 3))
activType = 'sigmoid'
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation=activType)(x)
model_final = Model(inputs=model.input,
outputs=predictions,
name='predictions')
cnn_pmml = KerasToPmml(model_final,model_name="MobileNetImage",description="Demo",\
copyright="Internal User",dataSet='image',predictedClasses=['dogs','cats'])
cnn_pmml.export(open('2classMBNet.pmml', "w"), 0)
img = image.load_img('nyoka/tests/resizedCat.png')
img = img_to_array(img)
img = preprocess_input(img)
imgtf = np.expand_dims(img, axis=0)
model_pred = model_final.predict(imgtf)
model_preds = {'dogs': model_pred[0][0], 'cats': model_pred[0][1]}
model_name = self.adapa_utility.upload_to_zserver('2classMBNet.pmml')
predictions, probabilities = self.adapa_utility.score_in_zserver(
model_name, 'nyoka/tests/resizedCat.png', 'DN')
self.assertEqual(
abs(probabilities['cats'] - model_preds['cats']) < 0.00001, True)
self.assertEqual(
abs(probabilities['dogs'] - model_preds['dogs']) < 0.00001, True)
def resnet_or_mobile():
# If you want to use your our weight pre trained##
#weights_res='/share_alpha/Submarina/pre_treined_features/new_dataset/resnet50_weights_tf_dim_ordering_tf_kernels.h5'
#resnet_50_model = applications.ResNet50(weights='imagenet', include_top=True)#,input_tensor=input_tensor)
mobile = applications.MobileNet(weights='imagenet', include_top=True)
mobile.summary()
return mobile
def models_factory(model_type, image_size):
if model_type == "vgg16":
base_model = applications.VGG16(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1], 3))
elif model_type == "vgg19":
base_model = applications.VGG19(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1], 3))
elif model_type == "resnet50":
base_model = applications.ResNet50(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1], 3))
elif model_type == "inceptionv3":
base_model = applications.InceptionV3(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1], 3))
elif model_type == "xception":
base_model = applications.Xception(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1], 3))
elif model_type == "mobilenet":
base_model = applications.MobileNet(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1], 3))
elif model_type == "inceptionresnetv2":
base_model = applications.InceptionResNetV2(weights='imagenet',
include_top=False,
input_shape=(image_size[0],
image_size[1],
3))
elif model_type == "nasnet":
base_model = applications.nasnet.NASNetLarge(
weights='imagenet',
include_top=False,
input_shape=(image_size[0], image_size[1], 3))
for layer in base_model.layers:
layer.trainable = False
top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(
Dense(1024, kernel_initializer='glorot_uniform', activation='relu'))
top_model.add(
Dense(1024, kernel_initializer='glorot_uniform', activation='relu'))
top_model.add(Dense(1, activation='sigmoid'))
model = Model(input=base_model.input, output=top_model(base_model.output))
return model, base_model
def get_imagenet_architecture(architecture, variant, size, alpha, output_layer, include_top=False, weights='imagenet'):
from keras import applications, Model
if include_top:
assert output_layer == 'last'
if size == 'auto':
size = get_image_size(architecture, variant, size)
shape = (size, size, 3)
if architecture == 'densenet':
if variant == 'auto':
variant = 'densenet-121'
if variant == 'densenet-121':
model = applications.DenseNet121(weights=weights, include_top=include_top, input_shape=shape)
elif variant == 'densenet-169':
model = applications.DenseNet169(weights=weights, include_top=include_top, input_shape=shape)
elif variant == 'densenet-201':
model = applications.DenseNet201(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'inception-resnet-v2':
model = applications.InceptionResNetV2(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'mobilenet':
model = applications.MobileNet(weights=weights, include_top=include_top, input_shape=shape, alpha=alpha)
elif architecture == 'mobilenet-v2':
model = applications.MobileNetV2(weights=weights, include_top=include_top, input_shape=shape, alpha=alpha)
elif architecture == 'nasnet':
if variant == 'auto':
variant = 'large'
if variant == 'large':
model = applications.NASNetLarge(weights=weights, include_top=include_top, input_shape=shape)
else:
model = applications.NASNetMobile(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'resnet-50':
model = applications.ResNet50(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'vgg-16':
model = applications.VGG16(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'vgg-19':
model = applications.VGG19(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'xception':
model = applications.Xception(weights=weights, include_top=include_top, input_shape=shape)
elif architecture == 'inception-v3':
model = applications.InceptionV3(weights=weights, include_top=include_top, input_shape=shape)
if output_layer != 'last':
try:
if isinstance(output_layer, int):
layer = model.layers[output_layer]
else:
layer = model.get_layer(output_layer)
except Exception:
raise VergeMLError('layer not found: {}'.format(output_layer))
model = Model(inputs=model.input, outputs=layer.output)
return model
def get_mobilenet(): baseModel = applications.MobileNet(weights='imagenet', include_top=False, input_shape=(constants.IMG_HEIGHT, constants.IMG_WIDTH, 3)) mobileModel = Sequential() mobileModel.add(baseModel) mobileModel.add(GlobalAveragePooling2D()) mobileModel.add(Dense(512, activation = 'relu')) mobileModel.add(Dense(256, activation = 'relu')) mobileModel.add(Dropout(0.5)) mobileModel.add(Dense(constants.NUM_CLASSES, activation='softmax')) mobileModel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) return mobileModel
def setUpClass(self):
print("******* Unit Test for Keras *******")
model = applications.MobileNet(weights='imagenet',
include_top=False,
input_shape=(224, 224, 3))
activType = 'sigmoid'
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation=activType)(x)
self.model_final = Model(inputs=model.input,
outputs=predictions,
name='predictions')
def detector_mobilenet(win_h=32, win_w=32, win_ch=1, final_activation='sigmoid'):
initial_model = applications.MobileNet(include_top=False, input_shape=(win_h, win_w, win_ch), weights=None)
x = initial_model.output
x = Flatten()(x)
out = Dense(1, activation=final_activation)(x)
detector = Model(inputs=initial_model.input, outputs=out)
detector.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
detector.summary()
return detector, initial_model
def test_keras_01(self):
model = applications.MobileNet(weights='imagenet', include_top=False,input_shape = (224, 224,3))
activType='sigmoid'
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation=activType)(x)
model_final = Model(inputs =model.input, outputs = predictions,name='predictions')
cnn_pmml = KerasToPmml(model_final,model_name="MobileNet",description="Demo",\
copyright="Internal User",dataSet='image',predictedClasses=['cats','dogs'])
cnn_pmml.export(open('2classMBNet.pmml', "w"), 0)
reconPmmlObj=ny.parse('2classMBNet.pmml',True)
self.assertEqual(os.path.isfile("2classMBNet.pmml"),True)
self.assertEqual(len(model_final.layers), len(reconPmmlObj.DeepNetwork[0].NetworkLayer))
def get_model(name='VGG19'):
if name=='VGG16':
return applications.VGG16(),(224, 224)
elif name=='VGG19':
return applications.VGG19(),(224, 224)
elif name=='ResNet50':
return applications.ResNet50(),(224, 224)
elif name=='InceptionV3':
return applications.InceptionV3(),(299,299)
elif name=='MobileNet':
return applications.MobileNet(),(224, 224) # any>32*32
elif name=='Xception':
return applications.Xception(),(299,299)
else:
return None, None
def test_construction_mobilenet(self):
model = applications.MobileNet(weights = "imagenet", include_top=False,input_shape = (224, 224, 3))
x = model.output
x = layers.Flatten()(x)
x = layers.Dense(1024, activation="relu")(x)
x = layers.Dropout(0.5)(x)
x = layers.Dense(1024, activation="relu")(x)
predictions = layers.Dense(2, activation="softmax")(x)
model_final = models.Model(input = model.input, output = predictions)
model_final.compile(loss = "binary_crossentropy", optimizer = optimizers.SGD(lr=0.0001, momentum=0.9), metrics=["accuracy"])
pmmlObj=KerasToPmml(model_final,model_name="MobileNet",dataSet='image')
pmmlObj.export(open('mobilenet.pmml','w'),0)
reconPmmlObj=ny.parse('mobilenet.pmml',True)
self.assertEqual(os.path.isfile("mobilenet.pmml"),True)
self.assertEqual(len(model_final.layers), len(reconPmmlObj.DeepNetwork[0].NetworkLayer))
def test_keras_01(self):
model = applications.MobileNet(weights='imagenet', include_top=False,input_shape = (224, 224,3)) #last layer not included
activType='sigmoid'
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation=activType)(x)
model_final = Model(inputs =model.input, outputs = predictions,name='predictions')
cnn_pmml = KerasToPmml(model_final,predictedClasses=['cats','dogs'])
cnn_pmml.export(open('2classMBNet.pmml', "w"), 0)
self.assertEqual(os.path.isfile("2classMBNet.pmml"),True)
def __init__(self, outputs=37, output_layer='sigmoid', **kwargs):
super(MNet, self).__init__()
sizes = [(128, 128, 3), (4, 4, 1024), 1024, outputs]
# Load the pre-trained base model
base = applications.MobileNet(weights='imagenet',
include_top=False,
input_shape=sizes[0])
# Freeze the layers except the last ones
# for layer in base.layers[:-4]:
# layer.trainable = False
self.add(base)
self.add(layers.Flatten())
self.add(layers.Dense(sizes[-2], activation='relu'))
self.add(layers.Dense(sizes[-1], activation=output_layer))
def model_app(arch, input_tensor):
"""Loads the appropriate convolutional neural network (CNN) model
Args:
arch: String key for model to be loaded.
input_tensor: Keras tensor to use as image input for the model.
Returns:
model: The specified Keras Model instance with ImageNet weights loaded and without the top classification layer.
"""
# function that loads the appropriate model
if arch == 'Xception':
model = applications.Xception(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('Xception loaded')
elif arch == 'VGG16':
model = applications.VGG16(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('VGG16 loaded')
elif arch == 'VGG19':
model = applications.VGG19(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('VGG19 loaded')
elif arch == 'ResNet50':
model = applications.ResNet50(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('ResNet50 loaded')
elif arch == 'InceptionV3':
model = applications.InceptionV3(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('InceptionV3 loaded')
elif arch == 'InceptionResNetV2':
model = applications.InceptionResNetV2(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('InceptionResNetV2 loaded')
elif arch == 'MobileNet':
model = applications.MobileNet(input_shape=(224, 224, 3), weights='imagenet', include_top=False,
input_tensor=input_tensor)
print('MobileNet loaded')
elif arch == 'DenseNet121':
model = applications.DenseNet121(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('DenseNet121 loaded')
elif arch == 'NASNetLarge':
model = applications.NASNetLarge(weights='imagenet', include_top=False, input_tensor=input_tensor)
print('NASNetLarge loaded')
elif arch == 'MobileNetV2':
model = applications.MobileNetV2(input_shape=(224, 224, 3), weights='imagenet', include_top=False,
input_tensor=input_tensor)
print('MobileNetV2 loaded')
else:
print('Invalid model selected')
model = False
return model
def create_model(self, image_width, image_height, num_classes):
model = applications.MobileNet(weights="imagenet",
include_top=False,
input_shape=(image_width, image_height,
3))
x = model.output
x = Flatten()(x)
x = Dropout(0.6)(x)
x = Dense(num_classes)(x)
x = BatchNormalization()(x)
x = Activation('softmax')(x)
predictions = x
return Model(model.input, predictions)
def test_02_image_classifier_with_base64string_as_input(self):
model = applications.MobileNet(weights='imagenet',
include_top=False,
input_shape=(224, 224, 3))
activType = 'sigmoid'
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(2, activation=activType)(x)
model_final = Model(inputs=model.input,
outputs=predictions,
name='predictions')
cnn_pmml = KerasToPmml(model_final,model_name="MobileNetBase64",description="Demo",\
copyright="Internal User",dataSet='imageBase64',predictedClasses=['dogs','cats'])
cnn_pmml.export(open('2classMBNetBase64.pmml', "w"), 0)
img = image.load_img('nyoka/tests/resizedCat.png')
img = img_to_array(img)
img = preprocess_input(img)
imgtf = np.expand_dims(img, axis=0)
base64string = "data:float32;base64," + FloatBase64.from_floatArray(
img.flatten(), 12)
base64string = base64string.replace("\n", "")
csvContent = "imageBase64\n\"" + base64string + "\""
text_file = open("input.csv", "w")
text_file.write(csvContent)
text_file.close()
model_pred = model_final.predict(imgtf)
model_preds = {'dogs': model_pred[0][0], 'cats': model_pred[0][1]}
model_name = self.adapa_utility.upload_to_zserver(
'2classMBNetBase64.pmml')
predictions, probabilities = self.adapa_utility.score_in_zserver(
model_name, 'input.csv', 'DN')
self.assertEqual(
abs(probabilities['cats'] - model_preds['cats']) < 0.00001, True)
self.assertEqual(
abs(probabilities['dogs'] - model_preds['dogs']) < 0.00001, True)
def importPreTrainedNet(name):
""" Imports the model specified by name, with weights trained on imagenet.
"""
if name == 'VGG16':
model = applications.VGG16(include_top=False, weights='imagenet')
elif name == 'MobileNet':
# MobileNet needs its input shape defined.
model = applications.MobileNet(include_top=False,
weights='imagenet',
input_shape=(128, 128, 3),
pooling='avg')
elif name == 'InceptionV3':
model = applications.InceptionV3(include_top=False,
weights='imagenet',
pooling='avg')
else:
print('failed to load model')
sys.exit()
print('Using ', name, " as base network.")
return model
def MobileNet_mod(width,
height,
numLatitudes,
numLongitudes,
finalAct="softmax"):
base_model = applications.MobileNet(alpha=1.0,
depth_multiplier=1,
weights="imagenet",
include_top=False,
input_shape=(width, height, 3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
# x_lo = Flatten()(x)
# x = GlobalAveragePooling2D()(x)
# x_la = Dense(16, activation='relu', name='fc1_1')(x)
# x_la = Dense(512, activation='relu', name='fc2_1')(x_la)
x_la = Dense(numLatitudes)(x)
latitudeBranch = Activation(finalAct, name="latitude_output")(x_la)
# x_lo = Dense(64, activation='relu', name='fc1_2')(x)
# x_lo = Dense(512, activation='relu', name='fc2_2')(x_lo)
x_lo = Dense(numLongitudes)(x)
longitudeBranch = Activation(finalAct, name="longitude_output")(x_lo)
model = Model(inputs=base_model.input,
outputs=[latitudeBranch, longitudeBranch],
name='posenet')
for i, layer in enumerate(model.layers):
print(i, layer.name)
for layer in model.layers[:20]:
layer.trainable = False
return model
def getKeysByValue(dictOfElements, valueToFind):
listOfKeys = list()
listOfItems = dictOfElements.items()
for item in listOfItems:
if item[1] == valueToFind:
listOfKeys.append(item[0])
return listOfKeys
img_width = 128
nb_train_samples = 4125
nb_validation_samples = 466
batch_size = 1
class_nb = 120
model = applications.MobileNet(weights="imagenet", include_top=False, input_shape=(img_width, img_width, 3))
for layer in model.layers:
print(layer, layer.trainable)
x = model.output
x = Flatten()(x)
x = Dense(1024, activation="relu")(x)
x = Dropout(0.5)(x)
x = Dense(1024, activation="relu")(x)
predictions = Dense(class_nb, activation="softmax")(x)
# creating the final model
classifier = Model(input=model.input, output=predictions)
print(classifier.summary())
# changez le nom à chaque fois svp ↓
experiment_name = "INATURALIST_E25_Mobilenet8142_D8142Relu_D8142Sigmoids_Lr0.3"
tb_callback = TensorBoard("./logs/" + experiment_name, )
print("Model training will start soon")
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
# build the VGG16 network
model = applications.MobileNet(weights='imagenet',
input_shape=input_shape,
classes=8142,
include_top=False,
alpha=1.0,
depth_multiplier=1,
dropout=1e-3)
print('Model loaded.')
# build a classifier model to put on top of the convolutional model
top_model = Flatten(input_shape=model.output_shape[1:])(model.output)
top_model = Dense(8142, activation='relu')(top_model)
top_model = Dropout(0.5)(top_model)
top_model = Dense(8142, activation='sigmoid')(top_model)
super_model = Model(model.input, top_model)
# note that it is necessary to start with a fully-trained
# classifier, including the top classifier,
# in order to successfully do fine-tuning
def test_mobilenet_pooling():
model = applications.MobileNet(weights=None,
include_top=False,
pooling='avg')
assert model.output_shape == (None, 1024)
def test_mobilenet_no_top():
model = applications.MobileNet(weights=None, include_top=False)
assert model.output_shape == (None, None, None, 1024)
def test_mobilenet():
model = applications.MobileNet(weights=None)
assert model.output_shape == (None, 1000)
valid_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
valid_batches = valid_datagen.flow_from_directory(DATASET_PATH + '/test',
target_size=IMAGE_SIZE,
interpolation='bicubic',
class_mode='categorical',
shuffle=False,
batch_size=BATCH_SIZE)
lrelu = lambda x: tensorflow.keras.activations.relu(x, alpha=0.1)
#Now make our first convolutional layer, 32 filters, 3x3, default stride and padding \
#model = applications.VGG16(weights = "imagenet", include_top=False, input_shape=[150,150,3])
model = applications.MobileNet(weights=None,
include_top=False,
input_shape=(128, 128, 3),
pooling='avg',
alpha=.5,
depth_multiplier=1,
dropout=.2)
# Freeze the layers which you don't want to train. Here I am freezing the first 5 layers.
#for layer in model.layers[:51]:
#layer.trainable = False
print(model.summary())
x = model.output
x = Dense(128, activation='sigmoid')(x)
x = Dropout(.2)(x)
x = Dense(32)(x)
predictions = Dense(2, activation="softmax")(x)
model.load_weights("MobileNet_Gleason_weights.h5", by_name=True)
#Use Adam optimizer (instead of plain SGD), set learning rate to explore. \
def mySpatialModel(model_name,
spatial_size,
nb_classes,
channels,
weights_path=None):
input_tensor = Input(shape=(channels, spatial_size, spatial_size))
input_shape = (channels, spatial_size, spatial_size)
base_model = None
predictions = None
data_dim = 1024
if model_name == 'ResNet50':
input_tensor = Input(shape=(spatial_size, spatial_size, channels))
input_shape = (spatial_size, spatial_size, channels)
base_model = kerasApp.ResNet50(include_top=False,
input_tensor=input_tensor,
input_shape=input_shape,
weights=weights_path,
classes=nb_classes,
pooling=None)
x = base_model.output
# 添加自己的全链接分类层 method 1
#x = Flatten()(x)
#predictions = Dense(nb_classes, activation='softmax')(x)
#method 2
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'VGG16':
input_tensor = Input(shape=(spatial_size, spatial_size, channels))
input_shape = (spatial_size, spatial_size, channels)
base_model = kerasApp.VGG16(include_top=False,
input_tensor=input_tensor,
input_shape=input_shape,
weights=weights_path,
classes=nb_classes,
pooling=None)
x = base_model.output
x = GlobalAveragePooling2D()(
x) # add a global spatial average pooling layer
x = Dense(1024,
activation='relu')(x) # let's add a fully-connected layer
predictions = Dense(nb_classes,
activation='softmax')(x) # and a logistic layer
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'VGG19':
base_model = kerasApp.VGG19(include_top=False,
input_tensor=input_tensor,
input_shape=input_shape,
weights=weights_path,
classes=2,
pooling=None)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'InceptionV3':
input_tensor = Input(shape=(spatial_size, spatial_size, channels))
input_shape = (spatial_size, spatial_size, channels)
base_model = kerasApp.InceptionV3(weights=weights_path,
include_top=False,
pooling=None,
input_shape=input_shape,
classes=nb_classes)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'InceptionResNetV2':
input_tensor = Input(shape=(spatial_size, spatial_size, channels))
input_shape = (
spatial_size,
spatial_size,
channels,
)
base_model = kerasApp.InceptionResNetV2(weights=weights_path,
include_top=False,
pooling=None,
input_shape=input_shape,
classes=nb_classes)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
data_dim = 1536
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'Xception':
input_shape_xception = (spatial_size, spatial_size, channels)
base_model = kerasApp.Xception(weights=weights_path,
include_top=False,
pooling="avg",
input_shape=input_shape_xception,
classes=nb_classes)
x = base_model.output
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'DenseNet121':
base_model = kerasApp.DenseNet121(weights=weights_path,
include_top=False,
pooling=None,
input_shape=input_shape,
classes=nb_classes)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'DenseNet169':
base_model = kerasApp.DenseNet169(weights=weights_path,
include_top=False,
pooling=None,
input_shape=input_shape,
classes=nb_classes)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'DenseNet201':
base_model = kerasApp.DenseNet201(weights=weights_path,
include_top=False,
pooling=None,
input_shape=input_shape,
classes=nb_classes)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
elif model_name == 'MobileNet':
base_model = kerasApp.MobileNet(weights=weights_path,
include_top=False,
pooling=None,
input_shape=input_shape,
classes=nb_classes)
x = base_model.output
# 添加自己的全链接分类层
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)
data_dim = 512
predictions = Dense(nb_classes, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
else:
print("this model--[" + model_name + "]-- doesnt exist!")
# 冻结base_model所有层，这样就可以正确获得bottleneck特征
for layer in base_model.layers:
layer.trainable = True
# 训练模型
model = Model(inputs=base_model.input, outputs=predictions)
print('-------------当前base_model模型[' + model_name +
"]-------------------\n")
print('base_model层数目:' + str(len(base_model.layers)))
print('model模型层数目:' + str(len(model.layers)))
featureLayer = model.layers[len(model.layers) - 2]
print(featureLayer.output_shape)
print("data_dim:" + str(featureLayer.output_shape[1]))
print("---------------------------------------------\n")
#sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
# 绘制模型
#if plot_model:
# plot_model(model, to_file=model_name+'.png', show_shapes=True)
return model