Kaggle competition: Petals to the Metal - Flower Classification on TPU
<SOS> metric: macrofscore Kaggle data source: petals-to-the-metal-flower-classification-on-tpu<data_type_conversions>
!pip install -q efficientnet
def _check_numberic(data):
return pd.to_numeric(data[data.notna() ], errors='coerce' ).notnull().all()<import_modules>
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 )
from sklearn.preprocessing import LabelEncoder<count_missing_values>
np.set_printoptions(threshold=15, linewidth=80)
 IMAGE_SIZE = [512, 512]
AUTO = tf.data.experimental.AUTOTUNE
EPOCHS = 12
BATCH_SIZE = 32 * strategy.num_replicas_in_sync
test.isna().sum()<concatenate>
GCS_DS_PATH = KaggleDatasets().get_gcs_path('tpu-getting-started')
 GCS_PATH = GCS_DS_PATH + f'/tfrecords-jpeg-{IMAGE_SIZE[0]}x{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')
 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']
newdata=pd.concat([dataset,test],axis=0 )<count_missing_values>
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.'])
 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.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.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 )
newdata.SalePrice.isna().sum()<drop_column>
training_dataset = get_training_dataset()
validation_dataset = get_validation_dataset()
testing_dataset = get_test_dataset()
newdata=newdata.reset_index(drop=True )<drop_column>
print(f"Training dataset info: {training_dataset}")
 print(f"Validation dataset info: {validation_dataset}")
 print(f"Test set info: {testing_dataset}" )
del newdata['3SsnPorch']
del newdata['Condition2']
del newdata['GarageCars']
del newdata['LowQualFinSF']
del newdata['MiscVal']
del newdata['RoofMatl']
del newdata['Street']
del newdata['Utilities']<feature_engineering>
display_batch_of_images(next(iter(training_dataset.unbatch().batch(12))))
newdata['HalfBath']=[1 if val>0 else 0 for val in newdata['HalfBath']]
newdata['PoolArea']=[1 if val>0 else 0 for val in newdata['PoolArea']]<count_missing_values>
with strategy.scope() :
B7_base = efn.EfficientNetB7(weights='imagenet', include_top=False, input_shape=[*IMAGE_SIZE, 3])
 B7_base.trainable = True
set_trainable = False
for layer in B7_base.layers:
if layer == B7_base.layers[-96]:
set_trainable = True
if set_trainable:
layer.trainable = True
else:
layer.trainable = False
model = tf.keras.Sequential([
B7_base,
tf.keras.layers.GlobalAveragePooling2D() ,
tf.keras.layers.Dropout(0.15),
tf.keras.layers.Dense(720, activation = 'relu'),
tf.keras.layers.Dropout(0.15),
tf.keras.layers.Dense(len(CLASSES), activation='softmax')
 ])
 model.compile(
 optimizer = tf.keras.optimizers.Adam(learning_rate=0.0005, beta_1=0.9, beta_2=0.999, amsgrad=False),
loss = 'sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'] , 
)
 model.summary()
nacols=newdata.isna().sum()<filter>
NUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)
 STEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE
print(NUM_TRAINING_IMAGES)
 print(STEPS_PER_EPOCH)
 print(EPOCHS )
nacols[nacols>0]<feature_engineering>
history2 = model.fit(training_dataset,
validation_data=validation_dataset,
epochs=EPOCHS,
steps_per_epoch=STEPS_PER_EPOCH )
newdata['BsmtFinSF1']=newdata['BsmtFinSF1'].fillna(newdata['BsmtFinSF1'].median())
 newdata['BsmtFinSF2']=newdata['BsmtFinSF2'].fillna(newdata['BsmtFinSF2'].median())
 newdata['BsmtFullBath']=newdata['BsmtFullBath'].fillna(newdata['BsmtFullBath'].median())
 newdata['BsmtHalfBath']=newdata['BsmtHalfBath'].fillna(newdata['BsmtHalfBath'].median())
 newdata['BsmtUnfSF']=newdata['BsmtUnfSF'].fillna(newdata['BsmtUnfSF'].median())
 newdata['GarageArea']=newdata['GarageArea'].fillna(newdata['GarageArea'].median())
 newdata['TotalBsmtSF']=newdata['TotalBsmtSF'].fillna(newdata['TotalBsmtSF'].median() )<count_values>
model.save('model_9383' )
col='Exterior1st'
newdata[col]=newdata[col].fillna(newdata[col].value_counts().idxmax())
 col='Exterior2nd'
newdata[col]=newdata[col].fillna(newdata[col].value_counts().idxmax())
 col='Functional'
newdata[col]=newdata[col].fillna(newdata[col].value_counts().idxmax())
 col='KitchenQual'
newdata[col]=newdata[col].fillna(newdata[col].value_counts().idxmax())
 col='MSZoning'
newdata[col]=newdata[col].fillna(newdata[col].value_counts().idxmax())
 col='SaleType'
newdata[col]=newdata[col].fillna(newdata[col].value_counts().idxmax() )<count_missing_values>
test_ds = get_test_dataset(ordered=True)
 print('Computing predictions...')
 test_images_ds = test_ds.map(lambda image, idnum: image)
 probabilities = model.predict(test_images_ds)
 predictions = np.argmax(probabilities, axis=-1)
 print(predictions )
<filter><EOS> 
print('Generating submission.csv file...')
 NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
 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='',
)