saurabhpal97

#importing the required modules
from vis.visualization import visualize_activation
from vis.utils import utils
from keras import activations
from keras import applications
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (18,6)
#creating a VGG16 model using fully connected layers also because then we can 
#visualize the patterns for individual category

saurabhpal97

import numpy as np

from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Activation, Conv2D, MaxPooling2D
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from keras.activations import relu

saurabhpal97

from vis.visualization import visualize_saliency
from vis.utils import utils
from keras import activations
#read the image
image = io.imread('car.jpeg')
#plot the image
io.imshow(image)

saurabhpal97

from vis.visualization import visualize_cam
from vis.utils import utils
from keras import activations

# Utility to search for layer index by name. 
# Alternatively we can specify this as -1 since it corresponds to the last layer.
layer_idx = utils.find_layer_idx(model, 'predictions')
#read the image and plot it
image = io.imread('car.jpeg')
io.imshow(image)

saurabhpal97

#importing required libraries and functions
from keras.models import Model
#defining names of layers from which we will take the output
layer_names = ['block1_conv1','block2_conv1','block3_conv1','block4_conv2']
outputs = []
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
#extracting the output and appending to outputs
for layer_name in layer_names:
    intermediate_layer_model = Model(inputs=model.input,outputs=model.get_layer(layer_name).output)
    intermediate_output = intermediate_layer_model.predict(image)

saurabhpal97

def squeeze_excite_block(input, ratio=16):
    init = input
    channel_axis = 1 if K.image_data_format() == "channels_first" else -1
    filters = init._keras_shape[channel_axis]
    se_shape = (1, 1, filters)

    se = GlobalAveragePooling2D()(init)
    se = Reshape(se_shape)(se)
    se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
    se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)

saurabhpal97

#importing the required libraries
from keras.datasets import cifar10
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
#instantiating the OneHotEncoder
enc = OneHotEncoder()

#loading the CIFAR 10 dataset
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
enc.fit(y_train)

saurabhpal97

from keras.models import Sequential
from keras.layers import Input,Conv2D,BatchNormalization,MaxPooling2D,Dropout,Activation,Flatten,Dense
from keras import regularizers
from keras import models
from keras.callbacks import ModelCheckpoint

#we have 10 classes in the dataset
num_classes = 10
#define the input 
img_input = Input(shape=(32,32,3))

saurabhpal97

from keras.models import load_model
#load the model
test_model = load_model('weights-improvement-125-0.86.hdf5')
perf = test_model.evaluate(x_test,y_test)
print('Validation Loss - ',perf[0])

saurabhpal97

from keras.models import Sequential
from keras.layers import Input,Conv2D,BatchNormalization,MaxPooling2D,Dropout,Activation,Flatten
from keras import regularizers
from keras import models
from keras.callbacks import ModelCheckpoint

num_classes = 10
weight_decay = 1e-4

img_input = Input(shape=(32,32,3))