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  1. nitish11 revised this gist Oct 20, 2016. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion readme.md
    View file
    Original file line number Diff line number Diff line change
    @@ -21,7 +21,7 @@ model and usage demo: see `vgg-16_keras.py`

    weights: [vgg16_weights.h5](https://drive.google.com/file/d/0Bz7KyqmuGsilT0J5dmRCM0ROVHc/view?usp=sharing)

    Extended VGG model : see `vgg-extended-functional-api.py`
    Extended VGG model to get 256 features : see `vgg-extended-functional-api.py`

    * Loaded trained VGG model with weights given in above link
    * Added Extra layers(Dense layers of keras, also called Fully connected layer) in the VGG net by using keras functional API to extract features as per our requirement.
  2. nitish11 revised this gist Oct 20, 2016. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion readme.md
    View file
    Original file line number Diff line number Diff line change
    @@ -21,7 +21,7 @@ model and usage demo: see `vgg-16_keras.py`

    weights: [vgg16_weights.h5](https://drive.google.com/file/d/0Bz7KyqmuGsilT0J5dmRCM0ROVHc/view?usp=sharing)

    Extended VGG model : see `vgg-extended-functional-api-py`
    Extended VGG model : see `vgg-extended-functional-api.py`

    * Loaded trained VGG model with weights given in above link
    * Added Extra layers(Dense layers of keras, also called Fully connected layer) in the VGG net by using keras functional API to extract features as per our requirement.
  3. nitish11 revised this gist Oct 20, 2016. 1 changed file with 2 additions and 1 deletion.
    3 changes: 2 additions & 1 deletion readme.md
    View file
    Original file line number Diff line number Diff line change
    @@ -23,4 +23,5 @@ weights: [vgg16_weights.h5](https://drive.google.com/file/d/0Bz7KyqmuGsilT0J5dmR

    Extended VGG model : see `vgg-extended-functional-api-py`

    * Added Extra layers(Dense layers of keras) in the VGG net by using keras functional API to extract features as per our requirement.
    * Loaded trained VGG model with weights given in above link
    * Added Extra layers(Dense layers of keras, also called Fully connected layer) in the VGG net by using keras functional API to extract features as per our requirement.
  4. nitish11 revised this gist Oct 20, 2016. 1 changed file with 4 additions and 0 deletions.
    4 changes: 4 additions & 0 deletions readme.md
    View file
    Original file line number Diff line number Diff line change
    @@ -20,3 +20,7 @@ Please cite the paper if you use the models.
    model and usage demo: see `vgg-16_keras.py`

    weights: [vgg16_weights.h5](https://drive.google.com/file/d/0Bz7KyqmuGsilT0J5dmRCM0ROVHc/view?usp=sharing)

    Extended VGG model : see `vgg-extended-functional-api-py`

    * Added Extra layers(Dense layers of keras) in the VGG net by using keras functional API to extract features as per our requirement.
  5. nitish11 revised this gist Oct 20, 2016. 1 changed file with 163 additions and 0 deletions.
    163 changes: 163 additions & 0 deletions vgg-extended-functional-api.py
    View file
    Original file line number Diff line number Diff line change
    @@ -0,0 +1,163 @@

    '''
    @author: Nitish Bhardwaj
    Keras Testing model for getting features from the VGG net trained model
    '''

    from keras.models import Sequential, Model
    from keras.layers import Dense, Dropout, Activation, Flatten, Input
    from keras.layers import Convolution2D, MaxPooling2D,ZeroPadding2D
    from keras.optimizers import SGD
    import theano
    import os
    import cv2
    import numpy as np
    from sklearn.decomposition import PCA


    # input image dimensions
    img_rows, img_cols = 224, 224
    img_channels = 3
    nb_classes = 10
    image_path = 'bb.jpg'
    weights_path = 'weights.h5'



    #Keras deepnet VGG-trained model
    def load_trained_model():

    trained_model = Sequential()
    trained_model.add(ZeroPadding2D((1,1),input_shape=(3,img_rows,img_cols)))
    trained_model.add(Convolution2D(64, 3, 3, activation='relu'))
    trained_model.add(ZeroPadding2D((1,1)))
    trained_model.add(Convolution2D(64, 3, 3, activation='relu'))
    trained_model.add(MaxPooling2D((2,2), strides=(2,2)))

    trained_model.add(ZeroPadding2D((1,1)))
    trained_model.add(Convolution2D(128, 3, 3, activation='relu'))
    trained_model.add(ZeroPadding2D((1,1)))
    trained_model.add(Convolution2D(128, 3, 3, activation='relu'))
    trained_model.add(MaxPooling2D((2,2), strides=(2,2)))

    trained_model.add(ZeroPadding2D((1,1)))
    trained_model.add(Convolution2D(256, 3, 3, activation='relu'))
    trained_model.add(ZeroPadding2D((1,1)))
    trained_model.add(Convolution2D(256, 3, 3, activation='relu'))
    trained_model.add(ZeroPadding2D((1,1)))
    trained_model.add(Convolution2D(256, 3, 3, activation='relu'))
    trained_model.add(MaxPooling2D((2,2), strides=(2,2)))

    trained_model.add(ZeroPadding2D((1,1)))
    trained_model.add(Convolution2D(512, 3, 3, activation='relu'))
    trained_model.add(ZeroPadding2D((1,1)))
    trained_model.add(Convolution2D(512, 3, 3, activation='relu'))
    trained_model.add(ZeroPadding2D((1,1)))
    trained_model.add(Convolution2D(512, 3, 3, activation='relu'))
    trained_model.add(MaxPooling2D((2,2), strides=(2,2)))

    trained_model.add(ZeroPadding2D((1,1)))
    trained_model.add(Convolution2D(512, 3, 3, activation='relu'))
    trained_model.add(ZeroPadding2D((1,1)))
    trained_model.add(Convolution2D(512, 3, 3, activation='relu'))
    trained_model.add(ZeroPadding2D((1,1)))
    trained_model.add(Convolution2D(512, 3, 3, activation='relu'))
    trained_model.add(MaxPooling2D((2,2), strides=(2,2)))

    trained_model.add(Flatten())
    trained_model.add(Dense(4096, activation='relu'))
    trained_model.add(Dropout(0.5))
    trained_model.add(Dense(4096, activation='relu'))
    trained_model.add(Dropout(0.5))
    trained_model.add(Dense(1000, activation='softmax'))
    trained_model.load_weights(weights_path)
    return trained_model


    # Load pretrained model and adding keras functional api to add more layers and to extract 256 features
    def cnn():
    trained_model = load_trained_model()

    #VGG model
    main_input = Input(shape=(3,img_rows,img_cols), dtype='float32', name='main_input')
    zpad1 = ZeroPadding2D((1,1), input_shape=(3,img_rows,img_cols), name='zpad1')(main_input)
    conv1 = Convolution2D(64, 3, 3, name='conv1', activation='relu')(zpad1)
    zpad2 = ZeroPadding2D((1,1), name='zpad2')(conv1)
    conv2 = Convolution2D(64, 3, 3, name='conv2', activation='relu')(zpad2)
    maxp1 = MaxPooling2D((2,2), strides=(2,2), name='maxp1')(conv2)

    zpad3 = ZeroPadding2D((1,1), name='zpad3')(maxp1)
    conv3 = Convolution2D(128, 3, 3, name='conv3', activation='relu')(zpad3)
    zpad4 = ZeroPadding2D((1,1), name='zpad4')(conv3)
    conv4 = Convolution2D(128, 3, 3, name='conv4', activation='relu')(zpad4)
    maxp2 = MaxPooling2D((2,2), strides=(2,2), name='maxp2')(conv4)

    zpad5 = ZeroPadding2D((1,1), name='zpad5')(maxp2)
    conv5 = Convolution2D(256, 3, 3, name='conv5', activation='relu')(zpad5)
    zpad6 = ZeroPadding2D((1,1), name='zpad6')(conv5)
    conv6 = Convolution2D(256, 3, 3, name='conv6', activation='relu')(zpad6)
    zpad7 = ZeroPadding2D((1,1), name='zpad7')(conv6)
    conv7 = Convolution2D(256, 3, 3, name='conv7', activation='relu')(zpad7)
    maxp3 = MaxPooling2D((2,2), strides=(2,2), name='maxp3')(conv7)

    zpad8 = ZeroPadding2D((1,1), name='zpad8')(maxp3)
    conv8 = Convolution2D(512, 3, 3, name='conv8', activation='relu')(zpad8)
    zpad9 = ZeroPadding2D((1,1), name='zpad9')(conv8)
    conv9 = Convolution2D(512, 3, 3, name='conv9', activation='relu')(zpad9)
    zpad10 = ZeroPadding2D((1,1), name='zpad10')(conv9)
    conv10 = Convolution2D(512, 3, 3, name='conv10', activation='relu')(zpad10)
    maxp4 = MaxPooling2D((2,2), strides=(2,2), name='maxp4')(conv10)

    zpad11 = ZeroPadding2D((1,1), name='zpad11')(maxp4)
    conv11 = Convolution2D(512, 3, 3, name='conv11', activation='relu')(zpad11)
    zpad12 = ZeroPadding2D((1,1), name='zpad12')(conv11)
    conv12 = Convolution2D(512, 3, 3, name='conv12', activation='relu')(zpad12)
    zpad13 = ZeroPadding2D((1,1), name='zpad13')(conv12)
    conv13 = Convolution2D(512, 3, 3, name='conv13', activation='relu')(zpad13)
    maxp5 = MaxPooling2D((2,2), strides=(2,2), name='maxp5')(conv13)

    flatten = Flatten(name='flatten')(maxp5)
    dense1 = Dense(4096, activation='relu', name='dense1')(flatten)
    dropout1 = Dropout(0.5, name='dropout1')(dense1)
    dense2 = Dense(4096, activation='relu', name='dense2')(dropout1)
    dropout2 = Dropout(0.5, name='dropout2')(dense2)
    dense3 = Dense(1000, activation='relu', name='dense3')(dropout2)
    dropout3 = Dropout(0.5, name='dropout3')(dense3)
    dense4 = Dense(256, activation='relu', name='dense4')(dropout3)


    print "==Defining Model =="
    base_model = Model(input=[main_input], output=[dense4])
    for i,layer in enumerate(trained_model.layers):
    base_model.layers[i+1].set_weights(layer.get_weights())

    print "==Compiling Model =="
    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
    base_model.compile(optimizer=sgd, loss='categorical_crossentropy')

    #Getting model for intermediate output
    model = Model(input=base_model.input, output=base_model.get_layer('dense4').output)
    return model



    def get_features(model,image):
    #Getting features from the given image
    test_image = [cv2.resize(image,(img_cols,img_rows))]
    test_image = np.transpose(test_image, [0,3,1,2]).astype('float32')[:,:,:,:]

    print "==Getting Features=="
    out = model.predict(test_image)
    # print out.shape
    return out



    #Loading DeepNet model
    model = cnn()

    #Reading image
    frame = cv2.imread(image_path)

    #printing 256 features
    print get_features(model, frame)
  6. @baraldilorenzo baraldilorenzo revised this gist Jan 16, 2016. 1 changed file with 12 additions and 8 deletions.
    20 changes: 12 additions & 8 deletions vgg-16_keras.py
    View file
    Original file line number Diff line number Diff line change
    @@ -10,37 +10,37 @@ def VGG_16(weights_path=None):
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(Flatten())
    model.add(Dense(4096, activation='relu'))
    @@ -53,14 +53,18 @@ def VGG_16(weights_path=None):
    model.load_weights(weights_path)

    return model

    if __name__ == "__main__":
    im = cv2.resize(cv2.imread('cat.jpg'), (224, 224))
    im = cv2.resize(cv2.imread('cat.jpg'), (224, 224)).astype(np.float32)
    im[:,:,0] -= 103.939
    im[:,:,1] -= 116.779
    im[:,:,2] -= 123.68
    im = im.transpose((2,0,1))
    im = np.expand_dims(im, axis=0)

    # Test pretrained model
    model = VGG_16('vgg16_weights.h5')
    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(optimizer=sgd, loss='categorical_crossentropy')
    out = model.predict(im)
    out = model.predict(im)
    print np.argmax(out)
  7. @baraldilorenzo baraldilorenzo revised this gist Nov 9, 2015. 1 changed file with 18 additions and 20 deletions.
    38 changes: 18 additions & 20 deletions vgg-16_keras.py
    View file
    Original file line number Diff line number Diff line change
    @@ -6,56 +6,54 @@

    def VGG_16(weights_path=None):
    model = Sequential()
    model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, 3, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, 64, 3, 3, activation='relu'))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, 64, 3, 3, activation='relu'))
    model.add(Convolution2D(128, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, 128, 3, 3, activation='relu'))
    model.add(Convolution2D(128, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 128, 3, 3, activation='relu'))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 256, 3, 3, activation='relu'))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 256, 3, 3, activation='relu'))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 256, 3, 3, activation='relu'))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 512, 3, 3, activation='relu'))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 512, 3, 3, activation='relu'))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 512, 3, 3, activation='relu'))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 512, 3, 3, activation='relu'))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 512, 3, 3, activation='relu'))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))

    model.add(Flatten())
    model.add(Dense(512*7*7, 4096, activation='relu'))

    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(4096, 4096, activation='relu'))

    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(4096, 1000, activation='softmax'))
    model.add(Dense(1000, activation='softmax'))

    if weights_path:
    model.load_weights(weights_path)

    return model

    if __name__ == "__main__":
    im = cv2.resize(cv2.imread('cat.jpg'), (224, 224))
    im = im.transpose((2,0,1))
  8. @baraldilorenzo baraldilorenzo revised this gist Sep 6, 2015. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion readme.md
    View file
    Original file line number Diff line number Diff line change
    @@ -17,6 +17,6 @@ Please cite the paper if you use the models.

    ###Contents:

    model and usage demo: see `main.py`
    model and usage demo: see `vgg-16_keras.py`

    weights: [vgg16_weights.h5](https://drive.google.com/file/d/0Bz7KyqmuGsilT0J5dmRCM0ROVHc/view?usp=sharing)
  9. @baraldilorenzo baraldilorenzo revised this gist Sep 6, 2015. 2 changed files with 0 additions and 0 deletions.
    0 VGG16_keras.md → readme.md
    View file
    File renamed without changes.
    0 main.py → vgg-16_keras.py
    View file
    File renamed without changes.
  10. @baraldilorenzo baraldilorenzo created this gist Sep 6, 2015.
    22 changes: 22 additions & 0 deletions VGG16_keras.md
    View file
    Original file line number Diff line number Diff line change
    @@ -0,0 +1,22 @@
    ##VGG16 model for Keras

    This is the [Keras](http://keras.io/) model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

    It has been obtained by directly converting the [Caffe model](https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md) provived by the authors.

    Details about the network architecture can be found in the following arXiv paper:

    Very Deep Convolutional Networks for Large-Scale Image Recognition
    K. Simonyan, A. Zisserman
    arXiv:1409.1556


    In the paper, the VGG-16 model is denoted as configuration `D`. It achieves 7.5% top-5 error on ILSVRC-2012-val, 7.4% top-5 error on ILSVRC-2012-test.

    Please cite the paper if you use the models.

    ###Contents:

    model and usage demo: see `main.py`

    weights: [vgg16_weights.h5](https://drive.google.com/file/d/0Bz7KyqmuGsilT0J5dmRCM0ROVHc/view?usp=sharing)
    68 changes: 68 additions & 0 deletions main.py
    View file
    Original file line number Diff line number Diff line change
    @@ -0,0 +1,68 @@
    from keras.models import Sequential
    from keras.layers.core import Flatten, Dense, Dropout
    from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
    from keras.optimizers import SGD
    import cv2, numpy as np

    def VGG_16(weights_path=None):
    model = Sequential()
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, 3, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, 64, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, 64, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, 128, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 128, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 256, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), stride=(2,2)))

    model.add(Flatten())
    model.add(Dense(512*7*7, 4096, activation='relu'))

    model.add(Dropout(0.5))
    model.add(Dense(4096, 4096, activation='relu'))

    model.add(Dropout(0.5))
    model.add(Dense(4096, 1000, activation='softmax'))

    if weights_path:
    model.load_weights(weights_path)

    return model

    if __name__ == "__main__":
    im = cv2.resize(cv2.imread('cat.jpg'), (224, 224))
    im = im.transpose((2,0,1))
    im = np.expand_dims(im, axis=0)

    # Test pretrained model
    model = VGG_16('vgg16_weights.h5')
    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(optimizer=sgd, loss='categorical_crossentropy')
    out = model.predict(im)