Python keras.applications.MobileNet() Examples

The following are 19 code examples of keras.applications.MobileNet(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.applications , or try the search function .
Example #1
Source Project: age-gender-estimator-keras   Author: KinarR   File: mobile_net.py    License: MIT License 6 votes vote down vote up
def __call__(self):
        logging.debug("Creating model...")

        inputs = Input(shape=self._input_shape)
        model_mobilenet = MobileNet(input_shape=self._input_shape, alpha=self.alpha, depth_multiplier=1, dropout=1e-3,
                                    include_top=False, weights=self.weights, input_tensor=None, pooling=None)

        x = model_mobilenet(inputs)

        feat_a = GlobalAveragePooling2D()(x)
        feat_a = Dropout(0.5)(feat_a)
        feat_a = Dense(self.FC_LAYER_SIZE, activation="relu")(feat_a)

        pred_g_softmax = Dense(2, activation='softmax', name='gender')(feat_a)
        pred_a_softmax = Dense(self.num_neu, activation='softmax', name='age')(feat_a)

        model = Model(inputs=inputs, outputs=[pred_g_softmax, pred_a_softmax])

        return model 
Example #2
Source Project: nyoka   Author: nyoka-pmml   File: testScoreWithAdapaKeras.py    License: Apache License 2.0 5 votes vote down vote up
def setUpClass(cls):
        print("******* Unit Test for Keras *******")
        cls.adapa_utility = AdapaUtility()
        cls.data_utility = DataUtility()
        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)
        cls.model_final = Model(inputs =model.input, outputs = predictions,name='predictions') 
Example #3
Source Project: nyoka   Author: nyoka-pmml   File: testScoreWithAdapaKeras.py    License: Apache License 2.0 5 votes vote down vote up
def test_02_image_classifier_with_base64string_as_input(self):
        model = applications.MobileNet(weights='imagenet', include_top=False,input_shape = (80, 80,3))
        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/resizedTiger.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) 
Example #4
Source Project: nyoka   Author: nyoka-pmml   File: _validateSchema.py    License: Apache License 2.0 5 votes vote down vote up
def test_validate_keras_mobilenet(self):
        input_tensor = Input(shape=(224, 224, 3))
        model = MobileNet(weights="imagenet", input_tensor=input_tensor)
        file_name = "keras"+model.name+".pmml"
        pmml_obj = KerasToPmml(model,dataSet="image",predictedClasses=[str(i) for i in range(1000)])
        pmml_obj.export(open(file_name,'w'),0)
        self.assertEqual(self.schema.is_valid(file_name), True) 
Example #5
Source Project: nyoka   Author: nyoka-pmml   File: test_keras_to_pmml_UnitTest.py    License: Apache License 2.0 5 votes vote down vote up
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') 
Example #6
Source Project: nyoka   Author: nyoka-pmml   File: test_keras_to_pmml_UnitTest.py    License: Apache License 2.0 5 votes vote down vote up
def test_keras_01(self):

        cnn_pmml = KerasToPmml(self.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(self.model_final.layers), len(reconPmmlObj.DeepNetwork[0].NetworkLayer)) 
Example #7
Source Project: Pseudo-Label-Keras   Author: koshian2   File: mobilenet_pseudo_cifar.py    License: MIT License 5 votes vote down vote up
def create_cnn():
    net = MobileNet(input_shape=(128,128,3), weights=None, include_top=False)
    # upsampling(32->128)
    input = Input((32,32,3))
    x = UpSampling2D(4)(input)
    x = net(x)
    x = GlobalAveragePooling2D()(x)
    x = Dense(10, activation="softmax")(x)

    model = Model(input, x)
    model.summary()
    return model 
Example #8
Source Project: Pseudo-Label-Keras   Author: koshian2   File: mobilenet_transfer_pseudo_cifar.py    License: MIT License 5 votes vote down vote up
def create_cnn():
    net = MobileNet(input_shape=(128,128,3), include_top=False)
    # conv_pw_6から訓練させる(41)
    for i in range(41):
        net.layers[i].trainable = False
    # upsampling(32->128)
    input = Input((32,32,3))
    x = UpSampling2D(4)(input)
    x = net(x)
    x = GlobalAveragePooling2D()(x)
    x = Dense(10, activation="softmax")(x)

    model = Model(input, x)
    model.summary()
    return model 
Example #9
Source Project: Pseudo-Label-Keras   Author: koshian2   File: mobilenet_transfer_cifar.py    License: MIT License 5 votes vote down vote up
def create_cnn():
    net = MobileNet(input_shape=(128,128,3), include_top=False)
    # conv_pw_6から訓練させる(41)
    for i in range(41):
        net.layers[i].trainable = False
    # upsampling(32->128)
    input = Input((32,32,3))
    x = UpSampling2D(4)(input)
    x = net(x)
    x = GlobalAveragePooling2D()(x)
    x = Dense(10, activation="softmax")(x)

    model = Model(input, x)
    model.summary()
    return model 
Example #10
Source Project: Pseudo-Label-Keras   Author: koshian2   File: mobilenet_supervised_cifar.py    License: MIT License 5 votes vote down vote up
def create_cnn():
    net = MobileNet(input_shape=(128,128,3), weights=None, include_top=False)
    # upsampling(32->128)
    input = Input((32,32,3))
    x = UpSampling2D(4)(input)
    x = net(x)
    x = GlobalAveragePooling2D()(x)
    x = Dense(10, activation="softmax")(x)

    model = Model(input, x)
    model.summary()
    return model 
Example #11
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: applications_test.py    License: MIT License 5 votes vote down vote up
def test_mobilenet():
    app = applications.MobileNet
    last_dim = 1024
    _test_application_basic(app)
    _test_application_notop(app, last_dim)
    _test_application_variable_input_channels(app, last_dim)
    _test_app_pooling(app, last_dim) 
Example #12
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: applications_test.py    License: MIT License 5 votes vote down vote up
def test_mobilenet():
    app = applications.MobileNet
    last_dim = 1024
    _test_application_basic(app)
    _test_application_notop(app, last_dim)
    _test_application_variable_input_channels(app, last_dim)
    _test_app_pooling(app, last_dim) 
Example #13
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: applications_test.py    License: MIT License 5 votes vote down vote up
def test_mobilenet():
    app = applications.MobileNet
    last_dim = 1024
    _test_application_basic(app)
    _test_application_notop(app, last_dim)
    _test_application_variable_input_channels(app, last_dim)
    _test_app_pooling(app, last_dim) 
Example #14
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: applications_test.py    License: MIT License 5 votes vote down vote up
def test_mobilenet():
    app = applications.MobileNet
    last_dim = 1024
    _test_application_basic(app)
    _test_application_notop(app, last_dim)
    _test_application_variable_input_channels(app, last_dim)
    _test_app_pooling(app, last_dim) 
Example #15
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: applications_test.py    License: MIT License 5 votes vote down vote up
def test_mobilenet():
    app = applications.MobileNet
    last_dim = 1024
    _test_application_basic(app)
    _test_application_notop(app, last_dim)
    _test_application_variable_input_channels(app, last_dim)
    _test_app_pooling(app, last_dim) 
Example #16
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: applications_test.py    License: MIT License 5 votes vote down vote up
def test_mobilenet():
    app = applications.MobileNet
    last_dim = 1024
    _test_application_basic(app)
    _test_application_notop(app, last_dim)
    _test_application_variable_input_channels(app, last_dim)
    _test_app_pooling(app, last_dim) 
Example #17
Source Project: DeepLearning_Wavelet-LSTM   Author: hello-sea   File: applications_test.py    License: MIT License 5 votes vote down vote up
def test_mobilenet():
    app = applications.MobileNet
    last_dim = 1024
    _test_application_basic(app)
    _test_application_notop(app, last_dim)
    _test_application_variable_input_channels(app, last_dim)
    _test_app_pooling(app, last_dim) 
Example #18
Source Project: Mosaicer   Author: seongahjo   File: file_util.py    License: MIT License 5 votes vote down vote up
def make_model(model, image_size):
    if model == "inceptionv3":
        base_model = InceptionV3(include_top=False, input_shape=image_size + (3,))
    elif model == "vgg16" or model is None:
        base_model = VGG16(include_top=False, input_shape=image_size + (3,))
    elif model == "mobilenet":
        base_model = MobileNet(include_top=False, input_shape=image_size + (3,))
    return base_model 
Example #19
Source Project: vergeml   Author: mme   File: features.py    License: MIT License 4 votes vote down vote up
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