Kaggle competition: Flower Classification with TPUs
<SOS> metric: MacroFScore Kaggle data source: flower-classification-with-tpus<count_values>
import re
import numpy as np
import math
import pandas as pd
import random
import tensorflow.keras.backend as K
from kaggle_datasets import KaggleDatasets
from matplotlib import pyplot as plt
import gc
from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix
all_data['GarageFinish']=all_data['GarageFinish'].fillna('None')
 all_data['GarageFinish'].value_counts()<count_values>
print("Tensorflow version " + tf.__version__ )
all_data['GarageCond'].value_counts()<count_values>
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))
all_data['GarageCond']=all_data['GarageCond'].fillna('None')
 all_data['GarageCond'].value_counts()<count_values>
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
all_data['GarageQual'].value_counts()<count_values>
AUTO = tf.data.experimental.AUTOTUNE
AUTO
all_data['GarageQual']=all_data['GarageQual'].fillna('None')
 all_data['GarageQual'].value_counts()<count_values>
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 )
all_data['FireplaceQu'].value_counts()<count_values>
IMAGE_SIZE = [512, 512]
EPOCHS = 15
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
all_data['FireplaceQu']=all_data['FireplaceQu'].fillna('None')
 all_data['FireplaceQu'].value_counts()<count_values>
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]))
all_data['Fence'].value_counts()<count_values>
GCS_DS_PATH = KaggleDatasets().get_gcs_path()
all_data['Fence']=all_data['Fence'].fillna('None')
 all_data['Fence'].value_counts()<count_values>
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' )
all_data['Alley'].value_counts()<count_values>
SKIP_VALIDATION = True
if SKIP_VALIDATION:
TRAINING_FILENAMES = TRAINING_FILENAMES + VALIDATION_FILENAMES
all_data['Alley']=all_data['Alley'].fillna('None')
 all_data['Alley'].value_counts()<count_values>
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']
all_data['MiscFeature'].value_counts()<count_values>
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 = load_dataset(TRAINING_FILENAMES, labeled=True)
 dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
 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(ordered=False):
dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, 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.map(transform, num_parallel_calls=AUTO)
 dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
 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 = count_data_items(TRAINING_FILENAMES)
 NUM_VALIDATION_IMAGES = 0 if SKIP_VALIDATION else 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))
all_data['MiscFeature']=all_data['MiscFeature'].fillna('None')
 all_data['MiscFeature'].value_counts()<count_values>
models = []
histories = []
all_data['PoolQC'].value_counts()<count_values>
!pip install -q efficientnet
with strategy.scope() :
enet = efn.EfficientNetB7(
 input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3),
weights='imagenet',
include_top=False 
)
 enet.trainable = True
model = tf.keras.Sequential([
enet,
tf.keras.layers.GlobalAveragePooling2D() ,
tf.keras.layers.Dense(len(CLASSES), activation='softmax')
 ])
 model.compile(
 optimizer='adam',
loss = 'sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'] 
)
 model.summary()
models.append(model)
 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() 
)
 histories.append(history)
 if not SKIP_VALIDATION:
display_training_curves(history.history['loss'], history.history['val_loss'], 'loss', 211)
 display_training_curves(history.history['sparse_categorical_accuracy'], history.history['val_sparse_categorical_accuracy'], 'accuracy', 212 )
all_data['PoolQC']=all_data['PoolQC'].fillna('None')
 all_data['PoolQC'].value_counts()<data_type_conversions>
gc.collect()
all_data['MSSubClass'] = all_data['MSSubClass'].astype(str)
 all_data['OverallCond'] = all_data['OverallCond'].astype(str)
 all_data['BsmtFullBath'] = all_data['BsmtFullBath'].astype(str)
 all_data['BsmtHalfBath'] = all_data['BsmtHalfBath'].astype(str)
 all_data['YrSold'] = all_data['YrSold'].astype(str)
 all_data['MoSold'] = all_data['MoSold'].astype(str)
 all_data['GarageYrBlt'] = all_data['GarageYrBlt'].astype(str)
 all_data['YearBuilt'] = all_data['YearBuilt'].astype(str)
 all_data['YearRemodAdd'] = all_data['YearRemodAdd'].astype(str)
 all_data['GarageCars'] = all_data['GarageCars'].astype(str )<feature_engineering>
with strategy.scope() :
rnet = DenseNet201(
 input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3),
weights='imagenet',
include_top=False 
)
 rnet.trainable = True
model = tf.keras.Sequential([
rnet,
tf.keras.layers.GlobalAveragePooling2D() ,
tf.keras.layers.Dense(len(CLASSES), activation='softmax')
 ])
 model.compile(
 optimizer='adam',
loss = 'sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'] 
)
 model.summary()
models.append(model)
 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() 
)
 histories.append(history)
 if not SKIP_VALIDATION:
display_training_curves(history.history['loss'], history.history['val_loss'], 'loss', 211)
 display_training_curves(history.history['sparse_categorical_accuracy'], history.history['val_sparse_categorical_accuracy'], 'accuracy', 212 )
all_data['TotalSF']=all_data['TotalBsmtSF']+all_data['1stFlrSF']+all_data['2ndFlrSF']<categorify>
gc.collect()
cols =('ExterCond','HeatingQC', 'BsmtFinType1',
'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope',
'LotShape', 'PavedDrive', 'CentralAir', 'MSSubClass', 'OverallCond',
'YrSold', 'MoSold','GarageYrBlt','YearBuilt','YearRemodAdd', 'BsmtHalfBath','BsmtFullBath', 'GarageCars')
 for c in cols:
lbl = LabelEncoder()
lbl.fit(list(all_data[c].values))
all_data[c] = lbl.transform(list(all_data[c].values))<data_type_conversions>
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()
cm_probabilities =(models[0].predict(images_ds)+ models[1].predict(images_ds)) / 2
cm_predictions = np.argmax(cm_probabilities, axis=-1)
 print("Correct labels: ", cm_correct_labels.shape, cm_correct_labels)
 print("Predicted labels: ", cm_predictions.shape, cm_predictions )
all_data.FireplaceQu = all_data.FireplaceQu.astype('category', ordered=True, categories=['None','Po','Fa','TA','Gd','Ex'] ).cat.codes
all_data.BsmtQual = all_data.BsmtQual.astype('category', ordered=True, categories=['None','Fa','TA','Gd','Ex'] ).cat.codes
all_data.BsmtCond = all_data.BsmtCond.astype('category', ordered=True, categories=['None','Po','Fa','TA','Gd'] ).cat.codes
all_data.GarageQual = all_data.GarageQual.astype('category', ordered=True, categories=['None','Po','Fa','TA','Gd','Ex'] ).cat.codes
all_data.GarageCond = all_data.GarageCond.astype('category', ordered=True, categories=['None','Po','Fa','TA','Gd','Ex'] ).cat.codes
all_data.ExterQual = all_data.ExterQual.astype('category', ordered=True, categories=['Fa','TA','Gd','Ex'] ).cat.codes
all_data.PoolQC = all_data.PoolQC.astype('category', ordered=True, categories=['None','Fa','Gd','Ex'] ).cat.codes
all_data.KitchenQual = all_data.KitchenQual.astype('category', ordered=True, categories=['Fa','TA','Gd','Ex'] ).cat.codes<feature_engineering>
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))
for feat in skewness[abs(skewness)>0.5].index:
all_data[feat] = boxcox1p(all_data[feat], 0.15 )<categorify>
if not SKIP_VALIDATION:
cmat_norm =(cmat.T / cmat.sum(axis=1)).T
display_confusion_matrix(cmat_norm, score, precision, recall )
all_data = pd.get_dummies(all_data )<drop_column>
TTA_NUM = 5
probabilities = []
for i in range(TTA_NUM):
print('TTA Number: ',i)
 test_ds = get_test_dataset(ordered=True)
 test_images_ds = test_ds.map(lambda image, idnum: image)
 probabilities.append(models[0].predict(test_images_ds))
prob1 = np.mean(probabilities,axis =0 )
new_train = all_data[all_data['SalePrice'].notnull() ]
new_test=all_data[all_data['SalePrice'].isnull() ]
new_test.drop('SalePrice', axis=1, inplace=True )<prepare_x_and_y>
TTA_NUM = 5
probabilities = []
for i in range(TTA_NUM):
print('TTA Number: ',i)
 test_ds = get_test_dataset(ordered=True)
 test_images_ds = test_ds.map(lambda image, idnum: image)
 probabilities.append(models[1].predict(test_images_ds))
prob2 = np.mean(probabilities,axis =0 )
<compute_train_metric><EOS> 
test_ds = get_test_dataset(ordered=True)
 print('Computing predictions...')
 test_images_ds = test_ds.map(lambda image, idnum: image)
 probabilities =(prob1+prob2)/2
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='' )