from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np
from matplotlib.pyplot import imshow
model = VGG16(weights='imagenet', include_top=False)
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.engine import Input
input_shape = (224, 224, 3)
#custom_model is the model architecture for VGG16 model
custom_model = Sequential([
Conv2D(64, (3, 3), input_shape=input_shape, padding='same',
activation='relu'),
Conv2D(64, (3, 3), activation='relu', padding='same'),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
Conv2D(128, (3, 3), activation='relu', padding='same'),
Conv2D(128, (3, 3), activation='relu', padding='same',),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
Conv2D(256, (3, 3), activation='relu', padding='same',),
Conv2D(256, (3, 3), activation='relu', padding='same',),
Conv2D(256, (3, 3), activation='relu', padding='same',),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
Conv2D(512, (3, 3), activation='relu', padding='same',),
Conv2D(512, (3, 3), activation='relu', padding='same',),
Conv2D(512, (3, 3), activation='relu', padding='same',),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
Conv2D(512, (3, 3), activation='relu', padding='same',),
Conv2D(512, (3, 3), activation='relu', padding='same',),
Conv2D(512, (3, 3), activation='relu', padding='same',),
MaxPooling2D(pool_size=(2, 2), strides=(2, 2))
])
#prepare our input image to match input shape
img_path = 'mona.jpg'
img = image.load_img(img_path, target_size = (224,224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
# We only visualize image after passing it to the first convolution layer and relu layer
# So we create a new test_model_1 with only the first two layers and assign them the weights from
# pretrained imagenet weights
test_model_1 = Sequential()
test_model_1.add(Conv2D(64, (3, 3), input_shape=input_shape, padding='same',
activation='relu', weights=model.layers[1].get_weights()))
test_model_1.add(Conv2D(64, (3, 3), activation='relu', padding='same', weights=model.layers[2].get_weights()))
predict_1 = test_model_1.predict(x)
# rearrange the shape after passing through first two layers
image_array = np.transpose(predict_1, (0,3,1,2))[0]
#combine all 64 images to one single image
for j in range(8):
for i in range(8):
if i == 0:
temprow = image_array[8*j]
else:
temprow = np.concatenate((temprow, image_array[(8*j)+i]), axis=1)
if j == 0:
temp = temprow
else:
temp = np.concatenate((temp, temprow), axis=0)
imshow(temp)
