VGG-19 pre-trained model for Keras

readme.md

##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model 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-19 model is denoted as configuration E. It achieves 7.5% top-5 error on ILSVRC-2012-val, 7.3% top-5 error on ILSVRC-2012-test.

Please cite the paper if you use the models.

###Contents:

model and usage demo: see vgg-19_keras.py

weights: vgg19_weights.h5

vgg-19_keras.py

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_19(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, activation='relu'))
    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), 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(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    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(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    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(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(Flatten())
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    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)).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_19('vgg19_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)
    print np.argmax(out)

How can I used it for grey scaled image?

For gray scale we need to change the channel to 1 ...
For colored its 3...
We need to play around with the input parameter of the very first layer...(3->1)

I have 4 classes to predict . can i use VGG-16 model to predict classes .

My tensorflow version is 1.8.0

I cannot load the vgg-19 weight file.
This is the error i got:
You are trying to load a weight file containing 0 layers into a model with 19 layers.

My tensorflow version is 1.8.0

I cannot load the vgg-19 weight file.
This is the error i got:
You are trying to load a weight file containing 0 layers into a model with 19 layers.

I have the same error. How to fix it?

I am getting this error, can you help me resolve the error. I haven't change the code

Using TensorFlow backend.
vgg-19_keras.py:11: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(64, (3, 3), activation="relu")
model.add(Convolution2D(64, 3, 3, activation='relu'))
WARNING:tensorflow:From /opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
vgg-19_keras.py:13: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(64, (3, 3), activation="relu")
model.add(Convolution2D(64, 3, 3, activation='relu'))
vgg-19_keras.py:17: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(128, (3, 3), activation="relu")
model.add(Convolution2D(128, 3, 3, activation='relu'))
vgg-19_keras.py:19: UserWarning: Update your Conv2D call to the Keras 2 API: Conv2D(128, (3, 3), activation="relu")
model.add(Convolution2D(128, 3, 3, activation='relu'))
Traceback (most recent call last):
File "vgg-19_keras.py", line 73, in
model = VGG_19('vgg19_weights.h5')
File "vgg-19_keras.py", line 20, in VGG_19
model.add(MaxPooling2D((2,2), strides=(2,2)))
File "/opt/conda/envs/py2/lib/python2.7/site-packages/keras/engine/sequential.py", line 181, in add
output_tensor = layer(self.outputs[0])
File "/opt/conda/envs/py2/lib/python2.7/site-packages/keras/engine/base_layer.py", line 457, in call
output = self.call(inputs, **kwargs)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/keras/layers/pooling.py", line 205, in call
data_format=self.data_format)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/keras/layers/pooling.py", line 268, in _pooling_function
pool_mode='max')
File "/opt/conda/envs/py2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 3978, in pool2d
data_format=tf_data_format)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/ops/nn_ops.py", line 2748, in max_pool
name=name)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 5137, in max_pool
data_format=data_format, name=name)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
op_def=op_def)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1823, in init
control_input_ops)
File "/opt/conda/envs/py2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1662, in _create_c_op
raise ValueError(str(e))
ValueError: Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_2/MaxPool' (op: 'MaxPool') with input shapes: [?,1,112,128].

I got the same issue:

ValueError: You are trying to load a weight file containing 0 layers into a model with 19 layers.

@NimmiGhetia your problem is in the keras,
edit the
~/.keras/keras.json and change the "image_data_format" to "image_data_format": "channels_first"

***this my model run complete , i using vgg19 to train data cifar 10 ,you can refer it

from keras.datasets import cifar10
import cv2
import random
import numpy as np
from keras.utils import to_categorical
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
import tensorflow as tf
#load data
(x_train,y_train),(x_test,y_test)=cifar10.load_data()

train data load 0 to 2000

ind_train = random.sample(list(range(x_train.shape[0])), 2000)
x_train = x_train[ind_train]
y_train = y_train[ind_train]

test data load 0 to 2000

ind_test = random.sample(list(range(x_test.shape[0])), 2000)
x_test = x_test[ind_test]
y_test = y_test[ind_test]
#resize image
def resize_data(data):
data_upscale=np.zeros((data.shape[0],224,224,3))
for i,img in enumerate(data):
l_img=cv2.resize(img,dsize=(224,224),interpolation=cv2.INTER_CUBIC)
data_upscale[i]=l_img
return data_upscale

resize train and test data

x_train_r = resize_data(x_train)
x_test_r = resize_data(x_test)

make explained variable hot-encoded

train_y= to_categorical(y_train)
test_y = to_categorical(y_test)

def VGG_19(weights=None, include_top=True, classes=10,input_shape=(224,224,3)):
model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape=(224,224,3)))
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), 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), 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(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, 3, 3, activation='relu'))
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(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
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(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, 3, 3, activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
return model

model = VGG_19(input_shape = (224, 224, 3), classes = 10)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
history=model.fit(x_train_r , train_y, epochs=5, batch_size=20, validation_data=(x_test_r , test_y ))
model.summary()

I am looking for a simple example of a vgg16 model to classify 3D medical images. thank you for your help

HI，the h5 link is no longer valid. Could you fix it?

HI，the h5 link is no longer valid. Could you fix it?