Kaggle competition: SIIM-ISIC Melanoma Classification
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<load_from_csv>
!pip install -q efficientnet >> /dev/null
train = pd.read_csv(".. /input/train.csv")
 test = pd.read_csv(".. /input/test.csv")
<prepare_x_and_y>
import pandas as pd
import numpy as np
from kaggle_datasets import KaggleDatasets
import tensorflow as tf
import re
import math
import tensorflow.keras.backend as K
import efficientnet.tfkeras as efn
from sklearn.model_selection import KFold
from sklearn.metrics import roc_auc_score
import matplotlib.pyplot as plt
import tensorflow_addons as tfa
import gc
y_train = train["label"]
X_train = train.drop(labels = ["label"],axis = 1 )<split>
print(tf.__version__)
 print(efficientnet.__version__)
 print(sklearn.__version__)
 print(matplotlib.__version__)
 print(tfa.__version__ )
def prep_data(X_train, y_train, test):
X_train = X_train.astype('float32')/ 255
test = test.astype('float32')/255
X_train = X_train.values.reshape(-1,28,28,1)
 test = test.values.reshape(-1,28,28,1)
 y_train = keras.utils.np_utils.to_categorical(y_train)
 classes = y_train.shape[1]
X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size = 0.2, random_state = int(time.time()))
return X_train, y_train, X_test, y_test, classes, test<split>
DEVICE = "TPU"
SEED = 42
FOLDS = 5
IMG_SIZES = [384] * FOLDS
INC2019 = [0] * FOLDS
INC2018 = [1] * FOLDS
INC2020 = [1] * FOLDS
BATCH_SIZES = [64]*FOLDS
EPOCHS = [11]*FOLDS
EFF_NETS = [6] * FOLDS
WGTS = [1/FOLDS]*FOLDS
TTA = 11
X_train, y_train, X_test, y_test, out_neurons, test = prep_data(X_train, y_train, test )<choose_model_class>
if DEVICE == "TPU":
print("connecting to TPU...")
 try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Running on TPU ', tpu.master())
 except ValueError:
print("Could not connect to TPU")
 tpu = None
if tpu:
try:
print("initializing TPU...")
 tf.config.experimental_connect_to_cluster(tpu)
 tf.tpu.experimental.initialize_tpu_system(tpu)
 strategy = tf.distribute.experimental.TPUStrategy(tpu)
 print("TPU initialized")
 except _:
print("failed to initialize TPU")
 else:
DEVICE = "GPU"
if DEVICE != "TPU":
print("Using default strategy for CPU and single GPU")
 strategy = tf.distribute.get_strategy()
if DEVICE == "GPU":
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
 AUTO = tf.data.experimental.AUTOTUNE
REPLICAS = strategy.num_replicas_in_sync
print(f'REPLICAS: {REPLICAS}' )
model = Sequential([
Conv2D(32, kernel_size =(3, 3), padding = 'same', activation = 'relu', input_shape =(28,28,1)) ,
Conv2D(32, kernel_size =(3, 3), activation = 'relu', padding = 'same'),
MaxPool2D(pool_size =(2, 2)) ,
Dropout(0.25),
Conv2D(64, kernel_size =(3, 3), activation = 'relu', padding = 'same'),
Conv2D(64, kernel_size =(3, 3), activation = 'relu', padding = 'same'),
MaxPool2D(pool_size =(2, 2)) ,
Dropout(0.25),
Conv2D(128, kernel_size =(3, 3), activation = 'relu', padding = 'same'),
Conv2D(128, kernel_size =(3, 3), activation = 'relu', padding = 'same'),
MaxPool2D(pool_size =(2, 2)) ,
Dropout(0.25),
Conv2D(128, kernel_size =(3, 3), activation = 'relu', padding = 'same'),
Conv2D(128, kernel_size =(3, 3), activation = 'relu', padding = 'same'),
MaxPool2D(pool_size =(2, 2)) ,
Dropout(0.25),
Flatten() ,
Dense(512, activation = 'relu'),
Dropout(0.5),
Dense(256, activation = 'relu'),
Dropout(0.5),
Dense(out_neurons, activation = 'softmax')
 ])
 model.summary()
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
 model.compile(loss = 'categorical_crossentropy', optimizer = sgd , metrics = ['accuracy'] )<train_model>
GCS_PATH = [None]*FOLDS; GCS_PATH2 = [None]*FOLDS; GCS_PATH3 = [None]*FOLDS
for i,k in enumerate(IMG_SIZES):
GCS_PATH[i] = KaggleDatasets().get_gcs_path('melanoma-%ix%i'%(k,k))
GCS_PATH2[i] = KaggleDatasets().get_gcs_path('isic2019-%ix%i'%(k,k))
GCS_PATH3[i] = KaggleDatasets().get_gcs_path('malignant-v2-%ix%i'%(k,k))
files_train = np.sort(np.array(tf.io.gfile.glob(GCS_PATH[0] + '/train*.tfrec')))
 files_test = np.sort(np.array(tf.io.gfile.glob(GCS_PATH[0] + '/test*.tfrec')) )
model.fit(X_train, y_train,
batch_size = 512,
epochs = 180,
validation_data =(X_test, y_test),
verbose = 0);<predict_on_test>
ROT_ = 180.0
SHR_ = 2.0
HZOOM_ = 8.0
WZOOM_ = 8.0
HSHIFT_ = 8.0
WSHIFT_ = 8.0
result = model.evaluate(X_test, y_test, verbose = 0)
 print('Accuracy: ', result[1])
 print('Error: %.2f%%' %(100- result[1]*100))
y_pred = model.predict(test, verbose=0 )<save_to_csv>
def get_mat(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):
rotation = math.pi * rotation / 180.
 shear = math.pi * shear / 180.
 def get_3x3_mat(lst):
return tf.reshape(tf.concat([lst],axis=0), [3,3])
 c1 = tf.math.cos(rotation)
 s1 = tf.math.sin(rotation)
 one = tf.constant([1],dtype='float32')
 zero = tf.constant([0],dtype='float32')
 rotation_matrix = get_3x3_mat([c1, s1, zero,
-s1, c1, zero,
zero, zero, one])
 c2 = tf.math.cos(shear)
 s2 = tf.math.sin(shear)
 shear_matrix = get_3x3_mat([one, s2, zero,
zero, c2, zero,
zero, zero, one])
 zoom_matrix = get_3x3_mat([one/height_zoom, zero, zero,
zero, one/width_zoom, zero,
zero, zero, one])
 shift_matrix = get_3x3_mat([one, zero, height_shift,
zero, one, width_shift,
zero, zero, one])
 return K.dot(K.dot(rotation_matrix, shear_matrix),
K.dot(zoom_matrix, shift_matrix))
def transform(image, DIM=256):
XDIM = DIM%2
rot = ROT_ * tf.random.normal([1], dtype='float32')
 shr = SHR_ * tf.random.normal([1], dtype='float32')
 h_zoom = 1.0 + tf.random.normal([1], dtype='float32')/ HZOOM_
w_zoom = 1.0 + tf.random.normal([1], dtype='float32')/ WZOOM_
h_shift = HSHIFT_ * tf.random.normal([1], dtype='float32')
 w_shift = WSHIFT_ * tf.random.normal([1], dtype='float32')
 m = get_mat(rot,shr,h_zoom,w_zoom,h_shift,w_shift)
 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(m, 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] )
solution = np.argmax(y_pred,axis = 1)
 solution = pd.Series(solution, name="Label" ).astype(int)
 submission = pd.concat([pd.Series(range(1,28001),name = "ImageId"),solution],axis = 1)
 submission.to_csv("mnist_with_cnn.csv",index=False )<set_options>
@tf.function
def transform_grid_mark(image, inv_mat, image_shape):
h, w, c = image_shape
cx, cy = w//2, h//2
new_xs = tf.repeat(tf.range(-cx, cx, 1), h)
 new_ys = tf.tile(tf.range(-cy, cy, 1), [w])
 new_zs = tf.ones([h*w], dtype=tf.int32)
 old_coords = tf.matmul(inv_mat, tf.cast(tf.stack([new_xs, new_ys, new_zs]), tf.float32))
old_coords_x, old_coords_y = tf.round(old_coords[0, :] + tf.cast(w, tf.float32)//2.) , tf.round(old_coords[1, :] + tf.cast(h, tf.float32)//2.)
old_coords_x = tf.cast(old_coords_x, tf.int32)
 old_coords_y = tf.cast(old_coords_y, tf.int32)
 clip_mask_x = tf.logical_or(old_coords_x<0, old_coords_x>w-1)
 clip_mask_y = tf.logical_or(old_coords_y<0, old_coords_y>h-1)
 clip_mask = tf.logical_or(clip_mask_x, clip_mask_y)
 old_coords_x = tf.boolean_mask(old_coords_x, tf.logical_not(clip_mask))
old_coords_y = tf.boolean_mask(old_coords_y, tf.logical_not(clip_mask))
new_coords_x = tf.boolean_mask(new_xs+cx, tf.logical_not(clip_mask))
new_coords_y = tf.boolean_mask(new_ys+cy, tf.logical_not(clip_mask))
old_coords = tf.cast(tf.stack([old_coords_y, old_coords_x]), tf.int32)
 new_coords = tf.cast(tf.stack([new_coords_y, new_coords_x]), tf.int64)
 rotated_image_values = tf.gather_nd(image, tf.transpose(old_coords))
rotated_image_channel = list()
for i in range(c):
vals = rotated_image_values[:,i]
sparse_channel = tf.SparseTensor(tf.transpose(new_coords), vals, [h, w])
 rotated_image_channel.append(tf.sparse.to_dense(sparse_channel, default_value=0, validate_indices=False))
return tf.transpose(tf.stack(rotated_image_channel), [1,2,0])
 @tf.function
def random_rotate(image, angle, image_shape):
def get_rotation_mat_inv(angle):
angle = math.pi * angle / 180
cos_val = tf.math.cos(angle)
 sin_val = tf.math.sin(angle)
 one = tf.constant([1], tf.float32)
 zero = tf.constant([0], tf.float32)
 rot_mat_inv = tf.concat([cos_val, sin_val, zero,
-sin_val, cos_val, zero,
zero, zero, one], axis=0)
 rot_mat_inv = tf.reshape(rot_mat_inv, [3,3])
 return rot_mat_inv
angle = float(angle)* tf.random.normal([1],dtype='float32')
 rot_mat_inv = get_rotation_mat_inv(angle)
 return transform_grid_mark(image, rot_mat_inv, image_shape)
 @tf.function
def grid_mask(DIM=256):
h = tf.constant(DIM, dtype=tf.float32)
 w = tf.constant(DIM, dtype=tf.float32)
 image_height, image_width =(h, w)
 d1 = int(DIM / 6)
 d2 = int(DIM / 4)
 rotate_angle = 45
ratio = 0.4
hh = tf.math.ceil(tf.math.sqrt(h*h+w*w))
hh = tf.cast(hh, tf.int32)
 hh = hh+1 if hh%2==1 else hh
d = tf.random.uniform(shape=[], minval=d1, maxval=d2, dtype=tf.int32)
 l = tf.cast(tf.cast(d,tf.float32)*ratio+0.5, tf.int32)
 st_h = tf.random.uniform(shape=[], minval=0, maxval=d, dtype=tf.int32)
 st_w = tf.random.uniform(shape=[], minval=0, maxval=d, dtype=tf.int32)
 y_ranges = tf.range(-1 * d + st_h, -1 * d + st_h + l)
 x_ranges = tf.range(-1 * d + st_w, -1 * d + st_w + l)
 for i in range(0, hh//d+1):
s1 = i * d + st_h
s2 = i * d + st_w
y_ranges = tf.concat([y_ranges, tf.range(s1,s1+l)], axis=0)
 x_ranges = tf.concat([x_ranges, tf.range(s2,s2+l)], axis=0)
 x_clip_mask = tf.logical_or(x_ranges <0 , x_ranges > hh-1)
 y_clip_mask = tf.logical_or(y_ranges <0 , y_ranges > hh-1)
 clip_mask = tf.logical_or(x_clip_mask, y_clip_mask)
 x_ranges = tf.boolean_mask(x_ranges, tf.logical_not(clip_mask))
y_ranges = tf.boolean_mask(y_ranges, tf.logical_not(clip_mask))
hh_ranges = tf.tile(tf.range(0,hh), [tf.cast(tf.reduce_sum(tf.ones_like(x_ranges)) , tf.int32)])
 x_ranges = tf.repeat(x_ranges, hh)
 y_ranges = tf.repeat(y_ranges, hh)
 y_hh_indices = tf.transpose(tf.stack([y_ranges, hh_ranges]))
x_hh_indices = tf.transpose(tf.stack([hh_ranges, x_ranges]))
y_mask_sparse = tf.SparseTensor(tf.cast(y_hh_indices, tf.int64), tf.zeros_like(y_ranges), [hh, hh])
 y_mask = tf.sparse.to_dense(y_mask_sparse, 1, False)
 x_mask_sparse = tf.SparseTensor(tf.cast(x_hh_indices, tf.int64), tf.zeros_like(x_ranges), [hh, hh])
 x_mask = tf.sparse.to_dense(x_mask_sparse, 1, False)
 mask = tf.expand_dims(tf.clip_by_value(x_mask + y_mask, 0, 1), axis=-1)
 mask = random_rotate(mask, rotate_angle, [hh, hh, 1])
 mask = tf.image.crop_to_bounding_box(mask,(hh-tf.cast(h, tf.int32)) //2,(hh-tf.cast(w, tf.int32)) //2, tf.cast(image_height, tf.int32), tf.cast(image_width, tf.int32))
return mask
@tf.function
def apply_grid_mask(image, DIM=256):
mask = grid_mask(DIM=DIM)
 mask = tf.concat([mask, mask, mask], axis=-1)
 return image * tf.cast(mask, 'float32' )
np.random.seed(7)
 %matplotlib inline<load_from_csv>
def read_labeled_tfrecord(example):
tfrec_format = {
'image' : tf.io.FixedLenFeature([], tf.string),
'image_name' : tf.io.FixedLenFeature([], tf.string),
'patient_id' : tf.io.FixedLenFeature([], tf.int64),
'sex' : tf.io.FixedLenFeature([], tf.int64),
'age_approx' : tf.io.FixedLenFeature([], tf.int64),
'anatom_site_general_challenge': tf.io.FixedLenFeature([], tf.int64),
'diagnosis' : tf.io.FixedLenFeature([], tf.int64),
'target' : tf.io.FixedLenFeature([], tf.int64)
 }
example = tf.io.parse_single_example(example, tfrec_format)
 return example['image'], example['target']
def read_unlabeled_tfrecord(example, return_image_name):
tfrec_format = {
'image' : tf.io.FixedLenFeature([], tf.string),
'image_name' : tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(example, tfrec_format)
 return example['image'], example['image_name'] if return_image_name else 0
def prepare_image(img, augment=True, dim=256):
img = tf.image.decode_jpeg(img, channels=3)
 img = tf.cast(img, tf.float32)/ 255.0
if augment:
img = transform(img,DIM=dim)
 img = tf.image.random_flip_left_right(img)
 img = tf.image.random_saturation(img, 0.7, 1.3)
 img = tf.image.random_contrast(img, 0.8, 1.2)
 img = tf.image.random_brightness(img, 0.1)
 img = apply_grid_mask(img,DIM=dim)
 img = tf.reshape(img, [dim,dim, 3])
 return img
def count_data_items(filenames):
n = [int(re.compile(r"-([0-9]*)\." ).search(filename ).group(1))
for filename in filenames]
return np.sum(n )
train_path = '.. //input//train.csv'
test_path = '.. //input//test.csv'
train_df = pd.read_csv(train_path)
 test_df = pd.read_csv(test_path)
 train_df.info()
test_df.info()<count_missing_values>
def get_dataset(files, augment = False, shuffle = False, repeat = False,
labeled=True, return_image_names=True, batch_size=16, dim=256):
ds = tf.data.TFRecordDataset(files, num_parallel_reads=AUTO)
 ds = ds.cache()
if repeat:
ds = ds.repeat()
if shuffle:
ds = ds.shuffle(1024*8)
 opt = tf.data.Options()
opt.experimental_deterministic = False
ds = ds.with_options(opt)
 if labeled:
ds = ds.map(read_labeled_tfrecord, num_parallel_calls=AUTO)
 else:
ds = ds.map(lambda example: read_unlabeled_tfrecord(example, return_image_names),
num_parallel_calls=AUTO)
 ds = ds.map(lambda img, imgname_or_label:(prepare_image(img, augment=augment, dim=dim),
imgname_or_label),
num_parallel_calls=AUTO)
 ds = ds.batch(batch_size * REPLICAS)
 ds = ds.prefetch(AUTO)
 return ds
pd.options.display.max_rows = 1000
print(train_df.isnull().sum() )<count_missing_values>
EFNS = [efn.EfficientNetB0, efn.EfficientNetB1, efn.EfficientNetB2, efn.EfficientNetB3,
efn.EfficientNetB4, efn.EfficientNetB5, efn.EfficientNetB6]
def build_model(dim=128, ef=0):
inp = tf.keras.layers.Input(shape=(dim,dim,3))
base = EFNS[ef](input_shape=(dim,dim,3),weights='imagenet',include_top=False)
 x = base(inp)
 x = tf.keras.layers.GlobalAveragePooling2D()(x)
 x = tf.keras.layers.Dense(1,activation='sigmoid' )(x)
 model = tf.keras.Model(inputs=inp,outputs=x)
 opt = tfa.optimizers.RectifiedAdam()
loss = tf.keras.losses.BinaryCrossentropy(label_smoothing=0.05)
 model.compile(optimizer=opt,loss=loss,metrics=['AUC'])
 return model
print(test_df.isnull().sum() )<data_type_conversions>
def get_lr_callback(batch_size=8):
lr_start = 0.00001
lr_max = 0.0000013 * REPLICAS * batch_size
lr_min = 0.000001
lr_ramp_ep = 4
lr_sus_ep = 0
lr_decay = 0.85
def lrfn(epoch):
if epoch < lr_ramp_ep:
lr =(lr_max - lr_start)/ lr_ramp_ep * epoch + lr_start
elif epoch < lr_ramp_ep + lr_sus_ep:
lr = lr_max
else:
lr =(lr_max - lr_min)* lr_decay**(epoch - lr_ramp_ep - lr_sus_ep)+ lr_min
return lr
lr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=False)
 return lr_callback
X_train = X_train.astype('float32')
 X_test = X_test.astype('float32')
 X_train /= 255
X_test /= 255<choose_model_class>
VERBOSE = 1
DISPLAY_PLOT = True
skf = KFold(n_splits=FOLDS,shuffle=True,random_state=SEED)
 oof_pred = []; oof_tar = []; oof_val = []; oof_names = []; oof_folds = []
preds = np.zeros(( count_data_items(files_test),1))
for fold,(idxT,idxV)in enumerate(skf.split(np.arange(15))):
if DEVICE=='TPU':
if tpu: tf.tpu.experimental.initialize_tpu_system(tpu)
 print('
print(' 
(IMG_SIZES[fold],EFF_NETS[fold],BATCH_SIZES[fold]*REPLICAS))
files_train = tf.io.gfile.glob([GCS_PATH[fold] + '/train%.2i*.tfrec'%x for x in idxT])
 if INC2019[fold]:
files_train += tf.io.gfile.glob([GCS_PATH2[fold] + '/train%.2i*.tfrec'%x for x in idxT*2+1])
 print('
if INC2018[fold]:
files_train += tf.io.gfile.glob([GCS_PATH2[fold] + '/train%.2i*.tfrec'%x for x in idxT*2])
 print('
if INC2020[fold]:
files_train += tf.io.gfile.glob([GCS_PATH3[fold] + '/train%.2i*.tfrec'%x for x in idxT + 15])
 print('
np.random.shuffle(files_train); print('
files_valid = tf.io.gfile.glob([GCS_PATH[fold] + '/train%.2i*.tfrec'%x for x in idxV])
 files_test = np.sort(np.array(tf.io.gfile.glob(GCS_PATH[fold] + '/test*.tfrec')))
 K.clear_session()
with strategy.scope() :
model = build_model(dim=IMG_SIZES[fold],ef=EFF_NETS[fold])
 sv = tf.keras.callbacks.ModelCheckpoint(
 'fold-%i.h5'%fold, monitor='val_loss', verbose=0, save_best_only=True,
save_weights_only=True, mode='min', save_freq='epoch')
 print('Training...')
 history = model.fit(
 get_dataset(files_train, augment=True, shuffle=True, repeat=True,
dim=IMG_SIZES[fold], batch_size = BATCH_SIZES[fold]),
epochs=EPOCHS[fold], callbacks = [sv,get_lr_callback(BATCH_SIZES[fold])],
steps_per_epoch=count_data_items(files_train)/BATCH_SIZES[fold]//REPLICAS,
validation_data=get_dataset(files_valid,augment=False,shuffle=False,
repeat=False,dim=IMG_SIZES[fold]),
verbose=VERBOSE 
)
 print('Loading best model...')
 model.load_weights('fold-%i.h5'%fold)
 print('Save whole model...')
 model.save('fold-%i-all.h5'%fold)
 gc.collect()
print('Predicting OOF with TTA...')
 ds_valid = get_dataset(files_valid,labeled=False,return_image_names=False,augment=True,
repeat=True,shuffle=False,dim=IMG_SIZES[fold],batch_size=BATCH_SIZES[fold]*3)
 ct_valid = count_data_items(files_valid)
 STEPS = TTA * ct_valid/BATCH_SIZES[fold]/3/REPLICAS
pred = model.predict(ds_valid,steps=STEPS,verbose=VERBOSE)[:TTA*ct_valid,]
oof_pred.append(np.mean(pred.reshape(( ct_valid,TTA),order='F'),axis=1))
ds_valid = get_dataset(files_valid, augment=False, repeat=False, dim=IMG_SIZES[fold],
labeled=True, return_image_names=True)
 oof_tar.append(np.array([target.numpy() for img, target in iter(ds_valid.unbatch())]))
oof_folds.append(np.ones_like(oof_tar[-1],dtype='int8')*fold)
 ds = get_dataset(files_valid, augment=False, repeat=False, dim=IMG_SIZES[fold],
labeled=False, return_image_names=True)
 oof_names.append(np.array([img_name.numpy().decode("utf-8")for img, img_name in iter(ds.unbatch())]))
gc.collect()
print('Predicting Test with TTA...')
 ds_test = get_dataset(files_test,labeled=False,return_image_names=False,augment=True,
repeat=True,shuffle=False,dim=IMG_SIZES[fold],batch_size=BATCH_SIZES[fold]*3)
 ct_test = count_data_items(files_test)
 STEPS = TTA * ct_test/BATCH_SIZES[fold]/3/REPLICAS
pred = model.predict(ds_test,steps=STEPS,verbose=VERBOSE)[:TTA*ct_test,]
preds[:,0] += np.mean(pred.reshape(( ct_test,TTA),order='F'),axis=1)* WGTS[fold]
gc.collect()
auc = roc_auc_score(oof_tar[-1],oof_pred[-1])
 oof_val.append(np.max(history.history['val_auc']))
print('
if DISPLAY_PLOT:
plt.figure(figsize=(15,5))
plt.plot(np.arange(EPOCHS[fold]),history.history['auc'],'-o',label='Train AUC',color='
plt.plot(np.arange(EPOCHS[fold]),history.history['val_auc'],'-o',label='Val AUC',color='
x = np.argmax(history.history['val_auc']); y = np.max(history.history['val_auc'])
 xdist = plt.xlim() [1] - plt.xlim() [0]; ydist = plt.ylim() [1] - plt.ylim() [0]
plt.scatter(x,y,s=200,color='
%.2f'%y,size=14)
 plt.ylabel('AUC',size=14); plt.xlabel('Epoch',size=14)
 plt.legend(loc=2)
 plt2 = plt.gca().twinx()
plt2.plot(np.arange(EPOCHS[fold]),history.history['loss'],'-o',label='Train Loss',color='
plt2.plot(np.arange(EPOCHS[fold]),history.history['val_loss'],'-o',label='Val Loss',color='
x = np.argmin(history.history['val_loss']); y = np.min(history.history['val_loss'])
 ydist = plt.ylim() [1] - plt.ylim() [0]
plt.scatter(x,y,s=200,color='
plt.ylabel('Loss',size=14)
 plt.title('FOLD %i - Image Size %i, EfficientNet B%i, inc2020=%i, inc2019=%i, inc2018=%i'% 
(fold+1,IMG_SIZES[fold],EFF_NETS[fold],INC2020[fold],INC2019[fold],INC2018[fold]),size=18)
 plt.legend(loc=3)
 plt.show()
gc.collect()
def inception_block(inputs):
tower_one = MaxPooling2D(( 3,3), strides=(1,1), padding='same' )(inputs)
 tower_one = Conv2D(6,(1,1), activation='relu', border_mode='same' )(tower_one)
 tower_two = Conv2D(6,(1,1), activation='relu', border_mode='same' )(inputs)
 tower_two = Conv2D(6,(3,3), activation='relu', border_mode='same' )(tower_two)
 tower_three = Conv2D(6,(1,1), activation='relu', border_mode='same' )(inputs)
 tower_three = Conv2D(6,(5,5), activation='relu', border_mode='same' )(tower_three)
 x = concatenate([tower_one, tower_two, tower_three], axis=3)
 return x
def inception_model(x_train):
inputs = Input(x_train.shape[1:])
 x = inception_block(inputs)
 x = Dropout(0.25 )(x)
 x = Conv2D(32,(3, 3), padding='same' )(x)
 x = Activation('relu' )(x)
 x = BatchNormalization()(x)
 x = Conv2D(32,(3, 3), padding='same' )(x)
 x = Activation('relu' )(x)
 x = BatchNormalization()(x)
 x = MaxPooling2D(pool_size=(2, 2))(x)
 x = Dropout(0.25 )(x)
 x = Flatten()(x)
 x = Dense(64, activation='relu' )(x)
 x = Dropout(0.5 )(x)
 predictions = Dense(10, activation='softmax' )(x)
 model = Model(input=inputs, output=predictions)
 model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
 return model<choose_model_class>
oof = np.concatenate(oof_pred); true = np.concatenate(oof_tar);
names = np.concatenate(oof_names); folds = np.concatenate(oof_folds)
 auc = roc_auc_score(true,oof)
 print('Overall OOF AUC with TTA = %.3f'%auc)
 df_oof = pd.DataFrame(dict(
 image_name = names, target=true, pred = oof, fold=folds))
df_oof.to_csv('oof.csv',index=False)
 df_oof.head()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] )<train_model>
ds = get_dataset(files_test, augment=False, repeat=False, dim=IMG_SIZES[fold],
labeled=False, return_image_names=True)
 image_names = np.array([img_name.numpy().decode("utf-8")
 for img, img_name in iter(ds.unbatch())] )
<compute_test_metric><EOS> 
submission = pd.DataFrame(dict(image_name=image_names, target=preds[:,0]))
submission = submission.sort_values('image_name')
 submission.to_csv('submission.csv', index=False)
 submission.head()