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VGG16_implemented.py
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import tensorflow as tf
from tensorflow.keras import layers, models
import cv2, numpy as np
import os
# define a VGG16 network
def VGG_16(weights_path=None):
model = models.Sequential()
model.add(layers.ZeroPadding2D((1,1),input_shape=(224,224, 3)))
model.add(layers.Convolution2D(64, (3, 3), activation='relu'))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2,2), strides=(2,2)))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(128, (3, 3), activation='relu'))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2,2), strides=(2,2)))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(256, (3, 3), activation='relu'))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(256, (3, 3), activation='relu'))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(256, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2,2), strides=(2,2)))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(512, (3, 3), activation='relu'))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(512, (3, 3), activation='relu'))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(512, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2,2), strides=(2,2)))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(512, (3, 3), activation='relu'))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(512, (3, 3), activation='relu'))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(512, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2,2), strides=(2,2)))
model.add(layers.Flatten())
#top layer of the VGG net
model.add(layers.Dense(4096, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(4096, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1000, activation='softmax'))
if weights_path:
model.load_weights(weights_path)
return model
im = cv2.resize(cv2.imread('cat.jpg'), (224, 224)).astype(np.float32)
#im = im.transpose((2,0,1))
im = np.expand_dims(im, axis=0)
# Test pretrained model
path_file = os.path.join(os.path.expanduser("~"), '.keras/models/vgg16_weights_tf_dim_ordering_tf_kernels.h5')
model = VGG_16(path_file)
model.summary()
model.compile(optimizer='sgd', loss='categorical_crossentropy')
out = model.predict(im)
print(np.argmax(out))