Kaggle competition: Flower Classification with TPUs
<SOS> metric: MacroFScore Kaggle data source: flower-classification-with-tpus<data_type_conversions>
!pip install -q efficientnet
train['Cabin'] = train['Cabin'].fillna('M' ).astype(str ).apply(lambda cabin: cabin[0] )<data_type_conversions>
print("Tensorflow version " + tf.__version__)
 AUTO = tf.data.experimental.AUTOTUNE
idx = train[train['Cabin'] == 'T'].index
train.loc[idx, 'Cabin'] = 'A'
train.Cabin.value_counts()
Test['Cabin'] = Test['Cabin'].fillna('M' ).astype(str ).apply(lambda cabin: cabin[0])
 Test.Cabin.value_counts()<categorify>
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Running on TPU ', tpu.master())
 except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
 tf.tpu.experimental.initialize_tpu_system(tpu)
 strategy = tf.distribute.experimental.TPUStrategy(tpu)
 else:
strategy = tf.distribute.get_strategy()
print("REPLICAS: ", strategy.num_replicas_in_sync )
train_categorical_features = ['Pclass', 'Sex','Title','Cabin', 'Embarked']
for feature in train_categorical_features:
dummies = pd.get_dummies(train[feature] ).add_prefix(feature + '_')
 train = train.join(dummies)
 train<categorify>
GCS_DS_PATH = KaggleDatasets().get_gcs_path()
test_categorical_features = ['Pclass', 'Sex','Title', 'Cabin', 'Embarked']
for feature in test_categorical_features:
dummies = pd.get_dummies(Test[feature] ).add_prefix(feature + '_')
 Test = Test.join(dummies)
 Test<import_modules>
IMAGE_SIZE = [331, 331]
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
SEED = 42
FOLDS = 5
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')+ tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')
 TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec')
 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']
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import learning_curve,GridSearchCV
from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression
import statsmodels.formula.api as sm
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC, LinearSVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.metrics import roc_curve, auc
import scikitplot as skplt
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_absolute_error, accuracy_score<drop_column>
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):

 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 = 13.0
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.'] )
drop_column = ['Pclass','Name','Sex','Cabin', 'Embarked','Surname','Title','Newname']
train.drop(drop_column, axis=1, inplace = True)
 drop_column = ['Pclass','Name','Sex','Cabin', 'Embarked','Surname','Title','Newname']
Test.drop(drop_column, axis=1, inplace = True )<drop_column>
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 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_tfrecord if labeled else read_unlabeled_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, do_aug=True):
dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
 if do_aug: dataset = dataset.map(transform, 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(dataset):
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):
n = [int(re.compile(r"-([0-9]*)\." ).search(filename ).group(1)) for filename in filenames]
return np.sum(n)
 NUM_TRAINING_IMAGES = int(count_data_items(TRAINING_FILENAMES)*(FOLDS-1.) /FOLDS)
 NUM_VALIDATION_IMAGES = int(count_data_items(TRAINING_FILENAMES)*(1./FOLDS))
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))
original_train_set_without_survived = train.drop("Survived", axis=1)
 orginal_train_set_with_only_survived = train["Survived"]<split>
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))
x_train, x_test, y_train, y_test = train_test_split(
 original_train_set_without_survived, orginal_train_set_with_only_survived, train_size=0.8, test_size=0.2, random_state=0)
 print(x_train.shape, y_train.shape)
 print(x_test.shape, y_test.shape )<normalization>
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
sc = StandardScaler()
x_train = sc.fit_transform(x_train)
 x_test = sc.transform(x_test)
 Test = sc.transform(Test )<choose_model_class>
MODEL_SELECT = 'Model1'
EPOCHS = 15
logreg = LogisticRegression()
logreg.fit(x_train,y_train)
 y_pred = logreg.predict(x_test)
 print("So, Our accuracy Score is: {}".format(round(accuracy_score(y_test,y_pred),8)) )<create_dataframe>
def get_model1() :
with strategy.scope() :
dn201 = tf.keras.applications.DenseNet201(weights='imagenet', include_top=False, input_shape=[*IMAGE_SIZE, 3])
 dn201.trainable = True
model1 = tf.keras.Sequential([
dn201,
tf.keras.layers.GlobalAveragePooling2D() ,
tf.keras.layers.Dense(len(CLASSES), activation='softmax')
 ])
 model1.compile(
 optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False),
loss = 'sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'] 
)
 return model1
def confusion_matrix_model(model_used):
cm=confusion_matrix(y_test,model_used.predict(x_test))
col=["Predicted Dead","Predicted Survived"]
cm=pd.DataFrame(cm)
 cm.columns=["Predicted Dead","Predicted Survived"]
cm.index=["Actual Dead","Actual Survived"]
cm[col]=np.around(cm[col].div(cm[col].sum(axis=1),axis=0),decimals=2)
 return cm
confusion_matrix_model(logreg )<choose_model_class>
def get_model2() :
with strategy.scope() :
enb7 = efn.EfficientNetB7(weights='noisy-student', include_top=False, input_shape=[*IMAGE_SIZE, 3])
 enb7.trainable = True
model2 = tf.keras.Sequential([
enb7,
tf.keras.layers.GlobalAveragePooling2D() ,
tf.keras.layers.Dense(len(CLASSES), activation='softmax')
 ])
 model2.compile(
 optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False),
loss = 'sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'] 
)
 return model2
k_fold = KFold(n_splits=10, shuffle=True, random_state=0 )<prepare_x_and_y>
LR_START = 0.0001
LR_MAX = 0.00005 * strategy.num_replicas_in_sync
LR_MIN = 0.00001
LR_RAMPUP_EPOCHS = 3
LR_SUSTAIN_EPOCHS = 0
LR_EXP_DECAY =.8
def lrfn(epoch):
if epoch < LR_RAMPUP_EPOCHS:
lr = np.random.random_sample() * 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 )
x = original_train_set_without_survived
y = orginal_train_set_with_only_survived<normalization>
es_callback = tf.keras.callbacks.EarlyStopping(min_delta=0, patience=5, verbose=1, mode='auto', restore_best_weights=True )
x = sc.fit_transform(x )<compute_train_metric>
def train_cross_validate(folds = 5):
histories = []
models = []
kfold = KFold(folds, shuffle = True, random_state = SEED)
 for f,(trn_ind, val_ind)in enumerate(kfold.split(TRAINING_FILENAMES)) :
print() ; print('
print('
print('
train_dataset = load_dataset(list(pd.DataFrame({'TRAINING_FILENAMES': TRAINING_FILENAMES} ).loc[trn_ind]['TRAINING_FILENAMES']), labeled = True)
 val_dataset = load_dataset(list(pd.DataFrame({'TRAINING_FILENAMES': TRAINING_FILENAMES} ).loc[val_ind]['TRAINING_FILENAMES']), labeled = True, ordered = True)
 if MODEL_SELECT is 'Ensamble' or MODEL_SELECT is 'Model1':
model1 = get_model1()
history1 = model1.fit(
 get_training_dataset(train_dataset),
steps_per_epoch = STEPS_PER_EPOCH,
epochs = EPOCHS,
callbacks = [lr_callback, es_callback],
validation_data = get_validation_dataset(val_dataset),
verbose=2 
)
 models.append(model1)
 histories.append(history1)
 if MODEL_SELECT is 'Ensamble' or MODEL_SELECT is 'Model2':
model2 = get_model2()
history2 = model2.fit(
 get_training_dataset(train_dataset),
steps_per_epoch = STEPS_PER_EPOCH,
epochs = EPOCHS,
callbacks = [lr_callback, es_callback],
validation_data = get_validation_dataset(val_dataset),
verbose=2 
)
 models.append(model2)
 histories.append(history2)
 return histories, models
def Acc_score(model):
return np.mean(cross_val_score(model,x,y,cv=k_fold,scoring="accuracy"))<compute_train_metric>
def train_and_predict(folds = 5):
test_ds = get_test_dataset(ordered=True)
 test_images_ds = test_ds.map(lambda image, idnum: image)
 print('Start training %i folds'%folds)
 histories, models = train_cross_validate(folds = folds)
 print('Computing predictions...')
 probabilities = np.average([models[i].predict(test_images_ds)for i in range(len(models)) ], axis = 0)
 predictions = np.argmax(probabilities, axis=-1)
 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='')
 return histories, models
logreg2=LogisticRegression()
print("Accuracy Scores: " + format(cross_val_score(logreg2,x,y,cv=k_fold,scoring="accuracy")))
 print(" ")
 print("Mean Accuracy Score: " + str(Acc_score(logreg2)))
 print(" ")
 print("Standard Deviation:", cross_val_score(logreg2,x,y,cv=k_fold,scoring="accuracy" ).std() )<compute_train_metric>
histories, models = train_and_predict(folds = FOLDS )
scores_auc = cross_val_score(logreg2, x, y, cv=k_fold, scoring='roc_auc')
 print("AUC score for 10 fold Cross Validation:", scores_auc)
 print(" ")
 print("Mean AUC score for 10 fold Cross Validation:", scores_auc.mean() )<categorify>
all_labels = []; all_prob = []; all_pred = []
kfold = KFold(FOLDS, shuffle = True, random_state = SEED)
 for j,(trn_ind, val_ind)in enumerate(kfold.split(TRAINING_FILENAMES)) :
print('Inferring fold',j+1,'validation images...')
 VAL_FILES = list(pd.DataFrame({'TRAINING_FILENAMES': TRAINING_FILENAMES} ).loc[val_ind]['TRAINING_FILENAMES'])
 NUM_VALIDATION_IMAGES = count_data_items(VAL_FILES)
 cmdataset = get_validation_dataset(load_dataset(VAL_FILES, labeled = True, ordered = True))
images_ds = cmdataset.map(lambda image, label: image)
 labels_ds = cmdataset.map(lambda image, label: label ).unbatch()
all_labels.append(next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES)) ).numpy())
 prob = models[j].predict(images_ds)
 all_prob.append(prob)
 all_pred.append(np.argmax(prob, axis=-1))
cm_correct_labels = np.concatenate(all_labels)
 cm_probabilities = np.concatenate(all_prob)
 cm_predictions = np.concatenate(all_pred)
 print("Correct labels: ", cm_correct_labels.shape, cm_correct_labels)
 print("Predicted labels: ", cm_predictions.shape, cm_predictions )
<compute_test_metric><EOS> 
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))