AmrutaKoshe

prediction=model.predict(prediction_image)
value=np.argmax(prediction)
move_name=mapper(value)
print("Prediction is {}.".format(move_name))

AmrutaKoshe

image=load_img("fruits/test/test/0030.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)

AmrutaKoshe

model.compile(optimizer='adam',
             loss='categorical_crossentropy',
             metrics=['accuracy'])

history = model.fit(train_generator, validation_data=valid_generator,
                   steps_per_epoch=train_generator.n//train_generator.batch_size,
                   validation_steps=valid_generator.n//valid_generator.batch_size,
                   epochs=10)

AmrutaKoshe

model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3,3),input_shape=(100,100,3), activation='relu', padding = 'same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding = 'same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding = 'same'))
model.add(MaxPooling2D(pool_size=(2, 2)))

AmrutaKoshe

train_generator = img_datagen.flow_from_directory(directory,
                                                 shuffle=True,
                                                 batch_size=32,
                                                 subset='training',
                                                 target_size=(100, 100))

valid_generator = img_datagen.flow_from_directory(directory,
                                                 shuffle=True,
                                                 batch_size=16,
                                                 subset='validation',

AmrutaKoshe

img_datagen = ImageDataGenerator(rescale=1./255,
                                vertical_flip=True,
                                horizontal_flip=True,
                                rotation_range=40,
                                width_shift_range=0.2,
                                height_shift_range=0.2,
                                zoom_range=0.1,
                                validation_split=0.2)
                                
test_datagen = ImageDataGenerator(rescale=1./255)

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)) 

AmrutaKoshe

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

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

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