AmrutaKoshe

history = model.fit_generator(train_generator,
         validation_data=(testx,testy),
         epochs=50,
         verbose=2)

AmrutaKoshe

model = Sequential()

model.add(Conv2D(32,(3,3),input_shape = (180,180,3)))
model.add(Activation('elu'))

model.add(Conv2D(64,(3,3)))
model.add(Activation('elu'))

model.add(MaxPool2D(pool_size = (2,2)))

AmrutaKoshe

train_datagen = ImageDataGenerator(shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)
train_datagen.fit(trainx)

train_generator = train_datagen.flow(trainx,trainy,batch_size = 32)

AmrutaKoshe

trainx,testx,trainy,testy=train_test_split(data,labels,test_size=0.2,random_state=44)

from tensorflow.keras.utils import normalize
trainx = normalize(trainx)
testx = normalize(testx)

AmrutaKoshe

labels1=to_categorical(labels0)
labels=np.array(labels1)

data=np.array(data)
test=np.array(test)

AmrutaKoshe

dataset=[]
testset=[]
count=0

for file in os.listdir(directory):
    path=os.path.join(directory,file)
    t=0
    for im in os.listdir(path):
        image=load_img(os.path.join(path,im), grayscale=False, color_mode='rgb', target_size=(180,180))
        image=img_to_array(image)

AmrutaKoshe

Breed = 'dog breed/Akita dog'
import os 
sub_class = os.listdir(Breed)

fig = plt.figure(figsize=(10,5))
for e in range(len(sub_class[:10])):
    plt.subplot(2,5,e+1)
    img = plt.imread(os.path.join(Breed,sub_class[e]))
    plt.imshow(img, cmap=plt.get_cmap('gray'))
    plt.axis('off')

AmrutaKoshe

Name=[]
for file in os.listdir(directory):
    Name+=[file]
print(Name)
print(len(Name))

AmrutaKoshe

!wget -N "https://cainvas-static.s3.amazonaws.com/media/user_data/AmrutaKoshe/dog_photos.zip"
!unzip -qo dog_photos.zip

AmrutaKoshe

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import wget
import os