iavinas

The Go language for experienced programmers

Why use Go?

• Like C, but with garbage collection, memory safety, and special mechanisms for concurrency
• Pointers but no pointer arithmetic
• No header files
• Simple, clean syntax
• Very fast native compilation (about as quick to edit code and restart as a dynamic language)
• Easy-to-distribute executables

iavinas

adb shell pm uninstall -k --user 0 com.heytap.market
adb shell pm uninstall -k --user 0 com.facebook.services
adb shell pm uninstall -k --user 0 com.facebook.katana
adb shell pm uninstall -k --user 0 com.facebook.system
adb shell pm uninstall -k --user 0 com.facebook.appmanager
adb shell pm uninstall -k --user 0 com.heytap.themestore
adb shell pm uninstall -k --user 0 com.google.android.keep
adb shell pm uninstall -k --user 0 com.google.android.youtube
adb shell pm uninstall -k --user 0 com.coloros.video
adb shell pm uninstall -k --user 0 com.android.bips

iavinas

from PIL import  Image
def run_app(img_path):
    img = Image.open(img_path)
    plt.imshow(img)
    plt.show()
    if dog_detector(img_path):
        print('Dog detected, Breed is ', end=' ')
        breed = Resnet50_predict_breed(img_path)
        print(breed)
    elif face_detector(img_path):

iavinas

Resnet50_predictions = [np.argmax(Resnet50_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Resnet50]

# report test accuracy
test_accuracy = 100*np.sum(np.array(Resnet50_predictions)==np.argmax(test_targets, axis=1))/len(Resnet50_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

# Test accuracy: 81.1005%

iavinas

Resnet50_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Resnet50.hdf5', 
                               verbose=1, save_best_only=True)

Resnet50_model.fit(train_Resnet50, train_targets, 
          validation_data=(valid_Resnet50, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)

iavinas

Resnet50_model = Sequential()
Resnet50_model.add(GlobalAveragePooling2D(input_shape=train_Resnet50.shape[1:]))
Resnet50_model.add(Dense(133, activation='softmax'))

Resnet50_model.summary()

iavinas

bottleneck_features = np.load('bottleneck_features/DogResnet50Data.npz')
train_Resnet50 = bottleneck_features['train']
valid_Resnet50 = bottleneck_features['valid']
test_Resnet50 = bottleneck_features['test']

iavinas

VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]

# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

#Test accuracy: 36.7225%

iavinas

VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', 
                               verbose=1, save_best_only=True)

VGG16_model.fit(train_VGG16, train_targets, 
          validation_data=(valid_VGG16, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)

iavinas

VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))

VGG16_model.summary()