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

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

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

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

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

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

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

AmrutaKoshe

load_img("dog_photos/test/image4.jpg",target_size=(180,180))

AmrutaKoshe

image=load_img("dog_photos/test/image4.jpg",target_size=(100,100))

image=img_to_array(image) 
image=image/255.0
prediction_image=np.array(image)
prediction_image= np.expand_dims(image, axis=0)

prediction=model.predict(prediction_image)
value=np.argmax(prediction)
move_name=mapper(value)

AmrutaKoshe

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

AmrutaKoshe

Name=[]
for file in os.listdir(directory):
    Name+=[file]
    
fruit_map = dict(zip(Name, [t for t in range(len(Name))]))
print(fruit_map)
r_fruit_map=dict(zip([t for t in range(len(Name))],Name))