Kaggle competition: Flower Classification with TPUs
<SOS> metric: MacroFScore Kaggle data source: flower-classification-with-tpus<load_from_csv>
!pip install -q efficientnet
original_test_df = pd.read_csv('.. /input/test.csv')
 passengers_id = original_test_df['PassengerId']
len(passengers_id )<predict_on_test>
print("Tensorflow version " + tf.__version__)
y_predict = []
y = []
for model in models_list:
y.append(model.predict(test_df[:]))
y = np.array(y)
 print(y.shape )<count_values>
AUTO = tf.data.experimental.AUTOTUNE
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
 tf.tpu.experimental.initialize_tpu_system(tpu)
 strategy = tf.distribute.experimental.TPUStrategy(tpu)
 GCS_DS_PATH = KaggleDatasets().get_gcs_path()
IMAGE_SIZE = [512, 512]
EPOCHS = 20
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
print(y[:,0], Counter(y[:,0] ).most_common(1))
print(y[:,1], Counter(y[:,1] ).most_common(1)) 
<prepare_output>
LR_START = 0.00001
LR_MAX = 0.00005 * strategy.num_replicas_in_sync
LR_MIN = 0.00001
LR_RAMPUP_EPOCHS = 5
LR_SUSTAIN_EPOCHS = 0
LR_EXP_DECAY =.8
def lrfn(epoch):
if epoch < LR_RAMPUP_EPOCHS:
lr =(LR_MAX - LR_START)/ LR_RAMPUP_EPOCHS * epoch + LR_START
elif epoch < LR_RAMPUP_EPOCHS + LR_SUSTAIN_EPOCHS:
lr = LR_MAX
else:
lr =(LR_MAX - LR_MIN)* LR_EXP_DECAY**(epoch - LR_RAMPUP_EPOCHS - LR_SUSTAIN_EPOCHS)+ LR_MIN
return lr
lr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=True)
 rng = [i for i in range(EPOCHS)]
y = [lrfn(x)for x in rng]
plt.plot(rng, y)
 print("Learning rate schedule: {:.3g} to {:.3g} to {:.3g}".format(y[0], max(y), y[-1]))
submission = pd.DataFrame(data=y_predict, columns=['PassengerId', 'Survived'])
 submission.head()<save_to_csv>
GCS_PATH_SELECT = {
192: GCS_DS_PATH + '/tfrecords-jpeg-192x192',
224: GCS_DS_PATH + '/tfrecords-jpeg-224x224',
331: GCS_DS_PATH + '/tfrecords-jpeg-331x331',
512: GCS_DS_PATH + '/tfrecords-jpeg-512x512'
}
GCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]
TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')
 VALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')
 TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec')
 SKIP_VALIDATION = True
if SKIP_VALIDATION:
TRAINING_FILENAMES = TRAINING_FILENAMES + VALIDATION_FILENAMES
VALIDATION_MISMATCHES_IDS = []
submission.to_csv('submission.csv', header=True, index=False )<load_from_csv>
CLASSES = ['pink primrose', 'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea', 'wild geranium', 'tiger lily', 'moon orchid', 'bird of paradise', 'monkshood', 'globe thistle',
'snapdragon', "colt's foot", 'king protea', 'spear thistle', 'yellow iris', 'globe-flower', 'purple coneflower', 'peruvian lily', 'balloon flower', 'giant white arum lily',
'fire lily', 'pincushion flower', 'fritillary', 'red ginger', 'grape hyacinth', 'corn poppy', 'prince of wales feathers', 'stemless gentian', 'artichoke', 'sweet william',
'carnation', 'garden phlox', 'love in the mist', 'cosmos', 'alpine sea holly', 'ruby-lipped cattleya', 'cape flower', 'great masterwort', 'siam tulip', 'lenten rose',
'barberton daisy', 'daffodil', 'sword lily', 'poinsettia', 'bolero deep blue', 'wallflower', 'marigold', 'buttercup', 'daisy', 'common dandelion',
'petunia', 'wild pansy', 'primula', 'sunflower', 'lilac hibiscus', 'bishop of llandaff', 'gaura', 'geranium', 'orange dahlia', 'pink-yellow dahlia',
'cautleya spicata', 'japanese anemone', 'black-eyed susan', 'silverbush', 'californian poppy', 'osteospermum', 'spring crocus', 'iris', 'windflower', 'tree poppy',
'gazania', 'azalea', 'water lily', 'rose', 'thorn apple', 'morning glory', 'passion flower', 'lotus', 'toad lily', 'anthurium',
'frangipani', 'clematis', 'hibiscus', 'columbine', 'desert-rose', 'tree mallow', 'magnolia', 'cyclamen ', 'watercress', 'canna lily',
'hippeastrum ', 'bee balm', 'pink quill', 'foxglove', 'bougainvillea', 'camellia', 'mallow', 'mexican petunia', 'bromelia', 'blanket flower',
'trumpet creeper', 'blackberry lily', 'common tulip', 'wild rose']
train = pd.read_csv("/kaggle/input/titanic/train.csv")
 test = pd.read_csv("/kaggle/input/titanic/test.csv" )<import_modules>
np.set_printoptions(threshold=15, linewidth=80)
 def batch_to_numpy_images_and_labels(data):
images, labels = data
numpy_images = images.numpy()
numpy_labels = labels.numpy()
if numpy_labels.dtype == object:
numpy_labels = [None for _ in enumerate(numpy_images)]
return numpy_images, numpy_labels
def title_from_label_and_target(label, correct_label):
if correct_label is None:
return CLASSES[label], True
correct =(label == correct_label)
 return "{} [{}{}{}]".format(CLASSES[label], 'OK' if correct else 'NO', u"\u2192" if not correct else '',
CLASSES[correct_label] if not correct else ''), correct
def display_one_flower(image, title, subplot, red=False, titlesize=16):
plt.subplot(*subplot)
 plt.axis('off')
 plt.imshow(image)
 if len(title)> 0:
plt.title(title, fontsize=int(titlesize)if not red else int(titlesize/1.2), color='red' if red else 'black', fontdict={'verticalalignment':'center'}, pad=int(titlesize/1.5))
return(subplot[0], subplot[1], subplot[2]+1)
 def display_batch_of_images(databatch, predictions=None, figsize = 13.0):

 images, labels = batch_to_numpy_images_and_labels(databatch)
 if labels is None:
labels = [None for _ in enumerate(images)]
rows = int(math.sqrt(len(images)))
 cols = len(images)//rows
FIGSIZE = figsize
SPACING = 0.1
subplot=(rows,cols,1)
 if rows < cols:
plt.figure(figsize=(FIGSIZE,FIGSIZE/cols*rows))
else:
plt.figure(figsize=(FIGSIZE/rows*cols,FIGSIZE))
for i,(image, label)in enumerate(zip(images[:rows*cols], labels[:rows*cols])) :
title = '' if label is None else CLASSES[label]
correct = True
if predictions is not None:
title, correct = title_from_label_and_target(predictions[i], label)
 dynamic_titlesize = FIGSIZE*SPACING/max(rows,cols)*40+3
subplot = display_one_flower(image, title, subplot, not correct, titlesize=dynamic_titlesize)
 plt.tight_layout()
if label is None and predictions is None:
plt.subplots_adjust(wspace=0, hspace=0)
 else:
plt.subplots_adjust(wspace=SPACING, hspace=SPACING)
 plt.show()
def display_confusion_matrix(cmat, score, precision, recall):
plt.figure(figsize=(15,15))
ax = plt.gca()
ax.matshow(cmat, cmap='Reds')
 ax.set_xticks(range(len(CLASSES)))
 ax.set_xticklabels(CLASSES, fontdict={'fontsize': 7})
 plt.setp(ax.get_xticklabels() , rotation=45, ha="left", rotation_mode="anchor")
 ax.set_yticks(range(len(CLASSES)))
 ax.set_yticklabels(CLASSES, fontdict={'fontsize': 7})
 plt.setp(ax.get_yticklabels() , rotation=45, ha="right", rotation_mode="anchor")
 titlestring = ""
if score is not None:
titlestring += 'f1 = {:.3f} '.format(score)
 if precision is not None:
titlestring += '
precision = {:.3f} '.format(precision)
 if recall is not None:
titlestring += '
recall = {:.3f} '.format(recall)
 if len(titlestring)> 0:
ax.text(101, 1, titlestring, fontdict={'fontsize': 18, 'horizontalalignment':'right', 'verticalalignment':'top', 'color':'
plt.show()
def display_training_curves(training, validation, title, subplot):
if subplot%10==1:
plt.subplots(figsize=(10,10), facecolor='
plt.tight_layout()
ax = plt.subplot(subplot)
 ax.set_facecolor('
ax.plot(training)
 ax.plot(validation)
 ax.set_title('model '+ title)
 ax.set_ylabel(title)
 ax.set_xlabel('epoch')
 ax.legend(['train', 'valid.'] )
import re
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.metrics import roc_curve, roc_auc_score
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier<feature_engineering>
def get_mat(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):
rotation = math.pi * rotation / 180.
 shear = math.pi * shear / 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])
 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])
 zoom_matrix = tf.reshape(tf.concat([one/height_zoom,zero,zero, zero,one/width_zoom,zero, zero,zero,one],axis=0),[3,3])
 shift_matrix = tf.reshape(tf.concat([one,zero,height_shift, zero,one,width_shift, zero,zero,one],axis=0),[3,3])
 return K.dot(K.dot(rotation_matrix, shear_matrix), K.dot(zoom_matrix, shift_matrix))
def transform(image,label):
DIM = IMAGE_SIZE[0]
XDIM = DIM%2
rot = 15.* tf.random.normal([1],dtype='float32')
 shr = 5.* tf.random.normal([1],dtype='float32')
 h_zoom = 1.0 + tf.random.normal([1],dtype='float32')/10.
 w_zoom = 1.0 + tf.random.normal([1],dtype='float32')/10.
 h_shift = 16.* tf.random.normal([1],dtype='float32')
 w_shift = 16.* 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]),label
def preprecess(data):
data.Cabin.fillna("N", inplace = True)
 data.loc[data.Cabin.str[0] == "A", "Cabin"] = "A"
data.loc[data.Cabin.str[0] == "B", "Cabin"] = "B"
data.loc[data.Cabin.str[0] == "C", "Cabin"] = "C"
data.loc[data.Cabin.str[0] == "D", "Cabin"] = "D"
data.loc[data.Cabin.str[0] == "E", "Cabin"] = "E"
data.loc[data.Cabin.str[0] == "F", "Cabin"] = "F"
data.loc[data.Cabin.str[0] == "G", "Cabin"] = "G"
data.loc[data.Cabin.str[0] == "T", "Cabin"] = "N"
data["Sex"].replace("female", 1, inplace=True)
 data["Sex"].replace("male", 0, inplace=True)
 data["Age"].fillna(data["Age"].median() ,inplace=True)
 data["Fare"].fillna(data["Fare"].median() ,inplace=True)
 data.Pclass.replace(1,"First",inplace=True)
 data.Pclass.replace(2,"Second",inplace=True)
 data.Pclass.replace(3,"Third",inplace=True)
 data = pd.get_dummies(data=data, columns=["Cabin","Embarked","Pclass"])
 return data
def age_detection(value):
if value < 18.0:
return "kid"
elif value >= 18.0 and value <=45.0:
return "adult"
else:
return "mature"
def group_title(data):
data["Title"] = data["Name"].map(lambda x: x.split(",")[1].split(".")[0].strip(" "))
data['Title'].replace('Master', "A", inplace=True)
 data['Title'].replace('Mr', "B", inplace=True)
 data['Title'].replace(['Ms','Mlle', 'Miss'], "C", inplace=True)
 data['Title'].replace(['Mme', 'Mrs'], "D", inplace=True)
 data['Title'].replace(['Dona', 'Lady', 'the Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'the'], "E", inplace=True)
 data = pd.get_dummies(data=data, columns=["Title"])
 return data
def outlier(data):
for column in ["Age", "Fare"]:
for sex in data.Sex.unique() :
sex_data = data[data["Sex"] == sex]
sex_column = sex_data[column]
Q1 = np.percentile(sex_column,25)
 Q3 = np.percentile(sex_column,75)
 IQR = Q3 - Q1
STEP = 1.5 * IQR
MAX_BORDER = Q3 + STEP
MIN_BORDER = Q1 - STEP
data.loc[(data["Sex"] == sex)&(data[column] > MAX_BORDER), column] = MAX_BORDER
data.loc[(data["Sex"] == sex)&(data[column] < MIN_BORDER), column] = MIN_BORDER
return data
def scaler(data):
data["Age"] = StandardScaler().fit_transform(data[["Age"]])
 data["Fare"] = StandardScaler().fit_transform(data[["Fare"]])
 return data<categorify>
def decode_image(image_data):
image = tf.image.decode_jpeg(image_data, channels=3)
 image = tf.cast(image, tf.float32)/ 255.0
image = tf.reshape(image, [*IMAGE_SIZE, 3])
 return image
def read_labeled_tfrecord(example):
LABELED_TFREC_FORMAT = {
"image": tf.io.FixedLenFeature([], tf.string),
"class": tf.io.FixedLenFeature([], tf.int64),
}
example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)
 image = decode_image(example['image'])
 label = tf.cast(example['class'], tf.int32)
 return image, label
def read_unlabeled_tfrecord(example):
UNLABELED_TFREC_FORMAT = {
"image": tf.io.FixedLenFeature([], tf.string),
"id": tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)
 image = decode_image(example['image'])
 idnum = example['id']
return image, idnum
def read_labeled_id_tfrecord(example):
LABELED_ID_TFREC_FORMAT = {
"image": tf.io.FixedLenFeature([], tf.string),
"class": tf.io.FixedLenFeature([], tf.int64),
"id": tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(example, LABELED_ID_TFREC_FORMAT)
 image = decode_image(example['image'])
 label = tf.cast(example['class'], tf.int32)
 idnum = example['id']
return image, label, idnum
def load_dataset(filenames, labeled=True, ordered=False):
ignore_order = tf.data.Options()
if not ordered:
ignore_order.experimental_deterministic = False
dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO)
 dataset = dataset.with_options(ignore_order)
 dataset = dataset.map(read_labeled_id_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)
 return dataset
def load_dataset_with_id(filenames, ordered=False):
ignore_order = tf.data.Options()
if not ordered:
ignore_order.experimental_deterministic = False
dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO)
 dataset = dataset.with_options(ignore_order)
 dataset = dataset.map(read_labeled_id_tfrecord, num_parallel_calls=AUTO)
 return dataset
def data_augment(image, label):
image = tf.image.random_flip_left_right(image)
 return image, label
def get_training_dataset() :
dataset = load_dataset(TRAINING_FILENAMES, labeled=True)
 dataset = dataset.filter(lambda image, label, idnum: tf.reduce_sum(tf.cast(idnum == VALIDATION_MISMATCHES_IDS, tf.int32)) ==0)
 dataset = dataset.map(lambda image, label, idnum: [image, label])
 dataset = dataset.map(transform, num_parallel_calls=AUTO)
 dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
 dataset = dataset.repeat()
dataset = dataset.shuffle(2048)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset
def get_validation_dataset(ordered=False):
dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)
 dataset = dataset.filter(lambda image, label, idnum: tf.reduce_sum(tf.cast(idnum == VALIDATION_MISMATCHES_IDS, tf.int32)) ==0)
 dataset = dataset.map(lambda image, label, idnum: [image, label])
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.cache()
dataset = dataset.prefetch(AUTO)
 return dataset
def get_validation_dataset_with_id(ordered=False):
dataset = load_dataset_with_id(VALIDATION_FILENAMES, ordered=ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.cache()
dataset = dataset.prefetch(AUTO)
 return dataset
def get_test_dataset(ordered=False):
dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 return dataset
def count_data_items(filenames):
dataset = load_dataset(filenames,labeled = False)
 dataset = dataset.map(lambda image, idnum: idnum)
 dataset = dataset.filter(lambda idnum: tf.reduce_sum(tf.cast(idnum == VALIDATION_MISMATCHES_IDS, tf.int32)) ==0)
 uids = next(iter(dataset.batch(21000)) ).numpy().astype('U')
 return len(np.unique(uids))
NUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)
 NUM_VALIDATION_IMAGES =(1 - SKIP_VALIDATION)* count_data_items(VALIDATION_FILENAMES)
 NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
 STEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE
print('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))
train = outlier(train)
 test = outlier(test)
 train["Age_Detection"] = train["Age"].apply(age_detection)
 test["Age_Detection"] = test["Age"].apply(age_detection)
 train = pd.get_dummies(data = train, columns = ["Age_Detection"])
 test = pd.get_dummies(data = test, columns = ["Age_Detection"])
 train = preprecess(train)
 train = group_title(train)
 test = preprecess(test)
 test = group_title(test)
 train = scaler(train)
 test = scaler(test)
 train.drop(["Name","Ticket"],axis = 1 ,inplace = True)
 test.drop(["Name","Ticket"],axis = 1 ,inplace = True )<drop_column>
if len(VALIDATION_MISMATCHES_IDS)>0:
dataset = load_dataset(TRAINING_FILENAMES,labeled = True)
 dataset = dataset.filter(lambda image, label, idnum: tf.reduce_sum(tf.cast(idnum == VALIDATION_MISMATCHES_IDS, tf.int32)) >0)
 dataset = dataset.map(lambda image, label, idnum: [image, label])
 imgs = next(iter(dataset.batch(len(VALIDATION_MISMATCHES_IDS))))
display_batch_of_images(imgs )
train.drop("PassengerId", axis = 1, inplace=True )<prepare_x_and_y>
with strategy.scope() :
enet = efn.EfficientNetB7(
 input_shape=(512, 512, 3),
weights='noisy-student',
include_top=False 
)
 model = tf.keras.Sequential([
enet,
tf.keras.layers.GlobalAveragePooling2D() ,
tf.keras.layers.Dense(len(CLASSES), activation='softmax')
 ])
 model.compile(
 optimizer=tf.keras.optimizers.Adam(lr=0.0001),
loss = 'sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'] 
)
 model.summary()
y = train.Survived
X = train.drop("Survived", axis=1 )<split>
history = model.fit(
 get_training_dataset() ,
steps_per_epoch=STEPS_PER_EPOCH,
epochs=EPOCHS,
callbacks=[lr_callback],
validation_data=None if SKIP_VALIDATION else get_validation_dataset()
)
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3, random_state = 1845 )<train_on_grid>
with strategy.scope() :
rnet = DenseNet201(
 input_shape=(512, 512, 3),
weights='imagenet',
include_top=False 
)
 model2 = tf.keras.Sequential([
rnet,
tf.keras.layers.GlobalAveragePooling2D() ,
tf.keras.layers.Dense(len(CLASSES), activation='softmax')
 ])
 model2.compile(
 optimizer=tf.keras.optimizers.Adam(lr=0.0001),
loss = 'sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'] 
)
 model2.summary()
xgb_params = {
'n_estimators' : [100,300,500,700],
'subsample' : [0.6, 0.8, 1.0],
'max_depth' : [3,4,5,6],
'learning_rate' : [0.1, 0.01, 0.02, 0.05],
'min_samples_split' : [2,5,10]
}
xgb = XGBClassifier()
xgb_cv_model = GridSearchCV(xgb, xgb_params, cv = 10, n_jobs = -1, verbose = 2 )<train_model>
history2 = model2.fit(
 get_training_dataset() ,
steps_per_epoch=STEPS_PER_EPOCH,
epochs=EPOCHS,
callbacks=[lr_callback],
validation_data=None if SKIP_VALIDATION else get_validation_dataset()
)
xgb_cv_model.fit(X_train, y_train )<find_best_params>
if not SKIP_VALIDATION:
cmdataset = get_validation_dataset(ordered=True)
 images_ds = cmdataset.map(lambda image, label: image)
 labels_ds = cmdataset.map(lambda image, label: label ).unbatch()
cm_correct_labels = next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES)) ).numpy()
m = model.predict(images_ds)
 m2 = model2.predict(images_ds)
 scores = []
for alpha in np.linspace(0,1,100):
cm_probabilities = alpha*m+(1-alpha)*m2
cm_predictions = np.argmax(cm_probabilities, axis=-1)
 scores.append(f1_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)) , average='macro'))
print("Correct labels: ", cm_correct_labels.shape, cm_correct_labels)
 print("Predicted labels: ", cm_predictions.shape, cm_predictions)
 plt.plot(scores)
 best_alpha = np.argmax(scores)/100
cm_probabilities = best_alpha*m+(1-best_alpha)*m2
cm_predictions = np.argmax(cm_probabilities, axis=-1)
 else:
best_alpha = 0.51
xgb_cv_model.best_params_<choose_model_class>
if not SKIP_VALIDATION:
cmdataset_with_id = get_validation_dataset_with_id(ordered=True)
 ids_ds = cmdataset_with_id.map(lambda image, label, idnum: idnum ).unbatch()
ids = next(iter(ids_ds.batch(NUM_VALIDATION_IMAGES)) ).numpy().astype('U')
 val_batch = iter(cmdataset.unbatch().batch(1))
noip = sum(cm_predictions!=cm_correct_labels)
 print('Number of incorrect predictions: ' + str(noip)+ '('+str(round(noip/NUM_VALIDATION_IMAGES*100,1)) +'%)')
 for fi in range(NUM_VALIDATION_IMAGES):
x = next(val_batch)
 if cm_predictions[fi] != cm_correct_labels[fi]:
print("Image id: '" + ids[fi] + "'")
 display_batch_of_images(x,np.array([cm_predictions[fi]]),figsize = 2 )
xgb = XGBClassifier(learning_rate = 0.1,
max_depth = 5,
n_estimators = 100,
subsample = 0.8)
<train_model>
if not SKIP_VALIDATION:
cmat = confusion_matrix(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)))
 score = f1_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)) , average='macro')
 precision = precision_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)) , average='macro')
 recall = recall_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)) , average='macro')
 display_confusion_matrix(cmat, score, precision, recall)
 print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))
<predict_on_test><EOS> 
test_ds = get_test_dataset(ordered=True)
 print('Computing predictions...')
 test_images_ds = test_ds.map(lambda image, idnum: image)
 probabilities = best_alpha*model.predict(test_images_ds)+(1-best_alpha)*model2.predict(test_images_ds)
 predictions = np.argmax(probabilities, axis=-1)
 print(predictions)
 print('Generating submission.csv file...')
 test_ids_ds = test_ds.map(lambda image, idnum: idnum ).unbatch()
test_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES)) ).numpy().astype('U')
 np.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='' )