Python keras.applications.densenet.DenseNet121() Examples

The following are 4 code examples of keras.applications.densenet.DenseNet121(). 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.densenet , or try the search function .
Example #1
Source File: pretrain_imagenet_cnn.py    From hyperspectral_deeplearning_review with GNU General Public License v3.0 5 votes vote down vote up 
def get_model_pretrain(arch):
    modlrate = 1
    if   "VGG16" in arch:       base_model = vgg16.VGG16
    elif "VGG19" in arch:       base_model = vgg19.VGG19
    elif "RESNET50" in arch:    base_model = resnet50.ResNet50
    elif "DENSENET121" in arch: base_model = densenet.DenseNet121
    elif "MOBILENET" in arch:
        base_model = mobilenet.MobileNet
        modlrate = 10
    else: print("model not avaiable"); exit()
    base_model = base_model(weights='imagenet', include_top=False)
    return base_model, modlrate
Example #2
Source File: test_keras_applications.py    From keras-onnx with MIT License 5 votes vote down vote up 
def test_DenseNet121(self):
        from keras.applications.densenet import DenseNet121
        model = DenseNet121(include_top=True, weights='imagenet')
        res = run_image(model, self.model_files, img_path)
        self.assertTrue(*res)
Example #3
Source File: models.py    From dsb2018_topcoders with MIT License 4 votes vote down vote up 
def get_densenet121_unet_softmax(input_shape, weights='imagenet'):
    blocks = [6, 12, 24, 16]
    img_input = Input(input_shape + (4,))
    
    x = ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
    x = Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                           name='conv1/bn')(x)
    x = Activation('relu', name='conv1/relu')(x)
    conv1 = x
    x = ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
    x = MaxPooling2D(3, strides=2, name='pool1')(x)
    x = dense_block(x, blocks[0], name='conv2')
    conv2 = x
    x = transition_block(x, 0.5, name='pool2')
    x = dense_block(x, blocks[1], name='conv3')
    conv3 = x
    x = transition_block(x, 0.5, name='pool3')
    x = dense_block(x, blocks[2], name='conv4')
    conv4 = x
    x = transition_block(x, 0.5, name='pool4')
    x = dense_block(x, blocks[3], name='conv5')
    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                           name='bn')(x)
    conv5 = x 
    
    conv6 = conv_block(UpSampling2D()(conv5), 320)
    conv6 = concatenate([conv6, conv4], axis=-1)
    conv6 = conv_block(conv6, 320)

    conv7 = conv_block(UpSampling2D()(conv6), 256)
    conv7 = concatenate([conv7, conv3], axis=-1)
    conv7 = conv_block(conv7, 256)

    conv8 = conv_block(UpSampling2D()(conv7), 128)
    conv8 = concatenate([conv8, conv2], axis=-1)
    conv8 = conv_block(conv8, 128)

    conv9 = conv_block(UpSampling2D()(conv8), 96)
    conv9 = concatenate([conv9, conv1], axis=-1)
    conv9 = conv_block(conv9, 96)

    conv10 = conv_block(UpSampling2D()(conv9), 64)
    conv10 = conv_block(conv10, 64)
    res = Conv2D(3, (1, 1), activation='softmax')(conv10)
    model = Model(img_input, res)
    
    if weights == 'imagenet':
        densenet = DenseNet121(input_shape=input_shape + (3,), weights=weights, include_top=False)
        w0 = densenet.layers[2].get_weights()
        w = model.layers[2].get_weights()
        w[0][:, :, [0, 1, 2], :] = 0.9 * w0[0][:, :, :3, :]
        w[0][:, :, 3, :] = 0.1 * w0[0][:, :, 1, :]
        model.layers[2].set_weights(w)
        for i in range(3, len(densenet.layers)):
            model.layers[i].set_weights(densenet.layers[i].get_weights())
            model.layers[i].trainable = False
    
    return model
Example #4
Source File: test_bench.py    From Keras-inference-time-optimizer with MIT License 4 votes vote down vote up 
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