Kaggle competition: Cassava Leaf Disease Classification
<SOS> metric: CategorizationAccuracy Kaggle data source: cassava-leaf-disease-classification<feature_engineering>
!pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git
sub2['ConfirmedCases'] = sub2['ConfirmedCases'].round(0)
 sub2['Fatalities'] = sub2['Fatalities'].round(0 ).abs()<save_to_csv>
def seed_everything(seed=0):
random.seed(seed)
 np.random.seed(seed)
 tf.random.set_seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 os.environ['TF_DETERMINISTIC_OPS'] = '1'
seed = 0
seed_everything(seed)
 warnings.filterwarnings('ignore' )
sub2.to_csv('submission.csv', index=False )<define_variables>
BATCH_SIZE = 16 * REPLICAS
HEIGHT = 512
WIDTH = 512
CHANNELS = 3
N_CLASSES = 5
TTA_STEPS = 5
<count_unique_values>
def data_augment(image, label):
p_spatial = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
 p_rotate = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
 p_pixel_1 = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
 p_pixel_2 = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
 p_crop = tf.random.uniform([], 0, 1.0, dtype=tf.float32)
 image = tf.image.random_flip_left_right(image)
 image = tf.image.random_flip_up_down(image)
 if p_spatial >.75:
image = tf.image.transpose(image)
 if p_rotate >.75:
image = tf.image.rot90(image, k=3)
 elif p_rotate >.5:
image = tf.image.rot90(image, k=2)
 elif p_rotate >.25:
image = tf.image.rot90(image, k=1)
 if p_crop >.6:
if p_crop >.9:
image = tf.image.central_crop(image, central_fraction=.5)
 elif p_crop >.8:
image = tf.image.central_crop(image, central_fraction=.6)
 elif p_crop >.7:
image = tf.image.central_crop(image, central_fraction=.7)
 else:
image = tf.image.central_crop(image, central_fraction=.8)
 elif p_crop >.3:
crop_size = tf.random.uniform([], int(HEIGHT*.6), HEIGHT, dtype=tf.int32)
 image = tf.image.random_crop(image, size=[crop_size, crop_size, CHANNELS])
 return image, label
<sort_values>
def transform_rotation(image, height, rotation):
DIM = height
XDIM = DIM%2
rotation = rotation * tf.random.uniform([1],dtype='float32')
 rotation = math.pi * rotation / 180.
 c1 = tf.math.cos(rotation)
 s1 = tf.math.sin(rotation)
 one = tf.constant([1],dtype='float32')
 zero = tf.constant([0],dtype='float32')
 rotation_matrix = tf.reshape(tf.concat([c1,s1,zero, -s1,c1,zero, zero,zero,one],axis=0),[3,3])
 x = tf.repeat(tf.range(DIM//2,-DIM//2,-1), DIM)
 y = tf.tile(tf.range(-DIM//2,DIM//2),[DIM])
 z = tf.ones([DIM*DIM],dtype='int32')
 idx = tf.stack([x,y,z])
 idx2 = K.dot(rotation_matrix,tf.cast(idx,dtype='float32'))
idx2 = K.cast(idx2,dtype='int32')
 idx2 = K.clip(idx2,-DIM//2+XDIM+1,DIM//2)
 idx3 = tf.stack([DIM//2-idx2[0,], DIM//2-1+idx2[1,]])
 d = tf.gather_nd(image, tf.transpose(idx3))
return tf.reshape(d,[DIM,DIM,3])
 def transform_shear(image, height, shear):
DIM = height
XDIM = DIM%2
shear = shear * tf.random.uniform([1],dtype='float32')
 shear = math.pi * shear / 180.
 one = tf.constant([1],dtype='float32')
 zero = tf.constant([0],dtype='float32')
 c2 = tf.math.cos(shear)
 s2 = tf.math.sin(shear)
 shear_matrix = tf.reshape(tf.concat([one,s2,zero, zero,c2,zero, zero,zero,one],axis=0),[3,3])
 x = tf.repeat(tf.range(DIM//2,-DIM//2,-1), DIM)
 y = tf.tile(tf.range(-DIM//2,DIM//2),[DIM])
 z = tf.ones([DIM*DIM],dtype='int32')
 idx = tf.stack([x,y,z])
 idx2 = K.dot(shear_matrix,tf.cast(idx,dtype='float32'))
idx2 = K.cast(idx2,dtype='int32')
 idx2 = K.clip(idx2,-DIM//2+XDIM+1,DIM//2)
 idx3 = tf.stack([DIM//2-idx2[0,], DIM//2-1+idx2[1,]])
 d = tf.gather_nd(image, tf.transpose(idx3))
return tf.reshape(d,[DIM,DIM,3] )
<drop_column>
def model_fn(input_shape, N_CLASSES):
inputs = L.Input(shape=input_shape, name='input_image')
 base_model = efn.EfficientNetB4(input_tensor=inputs,
include_top=False,
weights=None,
pooling='avg')
 x = L.Dropout (.5 )(base_model.output)
 output = L.Dense(N_CLASSES, activation='softmax', name='output' )(x)
 model = Model(inputs=inputs, outputs=output)
 return model
with strategy.scope() :
model = model_fn(( None, None, CHANNELS), N_CLASSES)
 model.summary()
<define_variables>
files_path = f'{database_base_path}test_images/'
test_size = len(os.listdir(files_path))
test_preds = np.zeros(( test_size, N_CLASSES))
for model_path in model_path_list:
print(model_path)
 K.clear_session()
model.load_weights(model_path)
 if TTA_STEPS > 0:
test_ds = get_dataset(files_path, tta=True ).repeat()
ct_steps = TTA_STEPS *(( test_size/BATCH_SIZE)+ 1)
 preds = model.predict(test_ds, steps=ct_steps, verbose=1)[:(test_size * TTA_STEPS)]
preds = np.mean(preds.reshape(test_size, TTA_STEPS, N_CLASSES, order='F'), axis=1)
 test_preds += preds / len(model_path_list)
 else:
test_ds = get_dataset(files_path, tta=False)
 x_test = test_ds.map(lambda image, image_name: image)
 test_preds += model.predict(x_test)/ len(model_path_list)
 test_preds = np.argmax(test_preds, axis=-1)
 test_names_ds = get_dataset(files_path)
 image_names = [img_name.numpy().decode('utf-8')for img, img_name in iter(test_names_ds.unbatch())]
<load_from_csv><EOS> 
submission = pd.DataFrame({'image_id': image_names, 'label': test_preds})
 submission.to_csv('submission.csv', index=False)
 display(submission.head() )