Python keras.applications.inception_resnet_v2.InceptionResNetV2() Examples

def build_model():
    base_model = InceptionResNetV2(include_top=False, weights='imagenet', input_shape=(img_size, img_size, channel),
                                   pooling='avg')
    image_input = base_model.input
    x = base_model.layers[-1].output
    out = Dense(embedding_size)(x)
    image_embedder = Model(image_input, out)

    input_a = Input((img_size, img_size, channel), name='anchor')
    input_p = Input((img_size, img_size, channel), name='positive')
    input_n = Input((img_size, img_size, channel), name='negative')

    normalize = Lambda(lambda x: K.l2_normalize(x, axis=-1), name='normalize')

    x = image_embedder(input_a)
    output_a = normalize(x)
    x = image_embedder(input_p)
    output_p = normalize(x)
    x = image_embedder(input_n)
    output_n = normalize(x)

    merged_vector = concatenate([output_a, output_p, output_n], axis=-1)

    model = Model(inputs=[input_a, input_p, input_n],
                  outputs=merged_vector)
    return model 

def create_model(train_generator, validation_generator):
    l2_reg = regularizers.l2({{loguniform(log(1e-6), log(1e-2))}})
    base_model = InceptionResNetV2(weights='imagenet', include_top=False)
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    x = Dropout({{uniform(0, 1)}})(x)
    x = Dense(1024, activation='relu', kernel_regularizer=l2_reg, activity_regularizer=l2_reg)(x)
    x = Dropout({{uniform(0, 1)}})(x)
    predictions = Dense(num_classes, activation='softmax', kernel_regularizer=l2_reg, activity_regularizer=l2_reg)(x)
    model = Model(inputs=base_model.input, outputs=predictions)

    model_weights_path = os.path.join('models', best_model)
    model.load_weights(model_weights_path)

    for i in range(int(len(base_model.layers) * {{uniform(0, 1)}})):
        layer = base_model.layers[i]
        layer.trainable = False

    adam = keras.optimizers.Adam(lr={{loguniform(log(1e-6), log(1e-3))}})
    model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=adam)

    # print(model.summary())

    model.fit_generator(
        train_generator,
        steps_per_epoch=num_train_samples // batch_size,
        validation_data=validation_generator,
        validation_steps=num_valid_samples // batch_size)

    score, acc = model.evaluate_generator(validation_generator)
    print('Test accuracy:', acc)
    return {'loss': -acc, 'status': STATUS_OK, 'model': model} 

def build_inception_resnet_V2(
    img_shape=(416, 416, 3),
    n_classes=16,
    l2_reg=0.0,
    load_pretrained=True,
    freeze_layers_from="base_model",
):
    # Decide if load pretrained weights from imagenet
    if load_pretrained:
        weights = "imagenet"
    else:
        weights = None

    # Get base model
    base_model = InceptionResNetV2(
        include_top=False, weights=weights, input_tensor=None, input_shape=img_shape
    )

    # Add final layers
    x = base_model.output
    x = AveragePooling2D((8, 8), strides=(8, 8), name="avg_pool")(x)
    x = Flatten(name="flatten")(x)
    x = Dense(512, activation="swish", name="dense_1", kernel_initializer="he_uniform")(
        x
    )
    x = Dropout(0.25)(x)
    predictions = Dense(
        n_classes,
        activation="softmax",
        name="predictions",
        kernel_initializer="he_uniform",
    )(x)

    # This is the model we will train
    model = Model(inputs=base_model.input, outputs=predictions)

    # Freeze some layers
    if freeze_layers_from is not None:
        if freeze_layers_from == "base_model":
            print("   Freezing base model layers")
            for layer in base_model.layers:
                layer.trainable = False
        else:
            for i, layer in enumerate(model.layers):
                print(i, layer.name)
            print("   Freezing from layer 0 to " + str(freeze_layers_from))
            for layer in model.layers[:freeze_layers_from]:
                layer.trainable = False
            for layer in model.layers[freeze_layers_from:]:
                layer.trainable = True

    return model 

def calculate_inception_score(images_path, batch_size=1, splits=10):
    # Create an instance of InceptionV3
    model = InceptionResNetV2()

    images = None
    for image_ in glob.glob(images_path):
        # Load image
        loaded_image = image.load_img(image_, target_size=(299, 299))

        # Convert PIL image to numpy ndarray
        loaded_image = image.img_to_array(loaded_image)

        # Another another dimension (Add batch dimension)
        loaded_image = np.expand_dims(loaded_image, axis=0)

        # Concatenate all images into one tensor
        if images is None:
            images = loaded_image
        else:
            images = np.concatenate([images, loaded_image], axis=0)

    # Calculate number of batches
    num_batches = (images.shape[0] + batch_size - 1) // batch_size

    probs = None

    # Use InceptionV3 to calculate probabilities
    for i in range(num_batches):
        image_batch = images[i * batch_size:(i + 1) * batch_size, :, :, :]
        prob = model.predict(preprocess_input(image_batch))

        if probs is None:
            probs = prob
        else:
            probs = np.concatenate([prob, probs], axis=0)

    # Calculate Inception scores
    divs = []
    split_size = probs.shape[0] // splits

    for i in range(splits):
        prob_batch = probs[(i * split_size):((i + 1) * split_size), :]
        p_y = np.expand_dims(np.mean(prob_batch, 0), 0)
        div = prob_batch * (np.log(prob_batch / p_y))
        div = np.mean(np.sum(div, 1))
        divs.append(np.exp(div))

    return np.mean(divs), np.std(divs) 

def build_model():
    base_model = InceptionResNetV2(weights='imagenet', include_top=False)
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    x = Dense(1024, activation='relu')(x)
    predictions = Dense(num_classes, activation='softmax')(x)
    model = Model(inputs=base_model.input, outputs=predictions)
    return model 

def get_tst_neural_net(type):
    model = None
    custom_objects = dict()
    if type == 'mobilenet_small':
        from keras.applications.mobilenet import MobileNet
        model = MobileNet((128, 128, 3), depth_multiplier=1, alpha=0.25, include_top=True, weights='imagenet')
    elif type == 'mobilenet':
        from keras.applications.mobilenet import MobileNet
        model = MobileNet((224, 224, 3), depth_multiplier=1, alpha=1.0, include_top=True, weights='imagenet')
    elif type == 'mobilenet_v2':
        from keras.applications.mobilenetv2 import MobileNetV2
        model = MobileNetV2((224, 224, 3), depth_multiplier=1, alpha=1.4, include_top=True, weights='imagenet')
    elif type == 'resnet50':
        from keras.applications.resnet50 import ResNet50
        model = ResNet50(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
    elif type == 'inception_v3':
        from keras.applications.inception_v3 import InceptionV3
        model = InceptionV3(input_shape=(299, 299, 3), include_top=True, weights='imagenet')
    elif type == 'inception_resnet_v2':
        from keras.applications.inception_resnet_v2 import InceptionResNetV2
        model = InceptionResNetV2(input_shape=(299, 299, 3), include_top=True, weights='imagenet')
    elif type == 'xception':
        from keras.applications.xception import Xception
        model = Xception(input_shape=(299, 299, 3), include_top=True, weights='imagenet')
    elif type == 'densenet121':
        from keras.applications.densenet import DenseNet121
        model = DenseNet121(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
    elif type == 'densenet169':
        from keras.applications.densenet import DenseNet169
        model = DenseNet169(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
    elif type == 'densenet201':
        from keras.applications.densenet import DenseNet201
        model = DenseNet201(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
    elif type == 'nasnetmobile':
        from keras.applications.nasnet import NASNetMobile
        model = NASNetMobile(input_shape=(224, 224, 3), include_top=True, weights='imagenet')
    elif type == 'nasnetlarge':
        from keras.applications.nasnet import NASNetLarge
        model = NASNetLarge(input_shape=(331, 331, 3), include_top=True, weights='imagenet')
    elif type == 'vgg16':
        from keras.applications.vgg16 import VGG16
        model = VGG16(input_shape=(224, 224, 3), include_top=False, pooling='avg', weights='imagenet')
    elif type == 'vgg19':
        from keras.applications.vgg19 import VGG19
        model = VGG19(input_shape=(224, 224, 3), include_top=False, pooling='avg', weights='imagenet')
    elif type == 'multi_io':
        model = get_custom_multi_io_model()
    elif type == 'multi_model_layer_1':
        model = get_custom_model_with_other_model_as_layer()
    elif type == 'multi_model_layer_2':
        model = get_small_model_with_other_model_as_layer()
    elif type == 'Conv2DTranspose':
        model = get_Conv2DTranspose_model()
    elif type == 'RetinaNet':
        model, custom_objects = get_RetinaNet_model()
    elif type == 'conv3d_model':
        model = get_simple_3d_model()
    return model, custom_objects