Kaggle competition: Digit Recognizer
outputs = compute_output_arrays(df_train, output_categories)
 inputs = compute_input_arrays(df_train, input_categories, tokenizer, MAX_SEQUENCE_LENGTH)
 test_inputs = compute_input_arrays(df_test, input_categories, tokenizer, MAX_SEQUENCE_LENGTH)
<split>
from keras.utils.np_utils import to_categorical
gkf = GroupKFold(n_splits=5 ).split(X=df_train.question_body, groups=df_train.question_body)
 valid_preds = []
test_preds = []
for fold,(train_idx, valid_idx)in enumerate(gkf):
if fold in [0, 2]:
train_inputs = [inputs[i][train_idx] for i in range(len(inputs)) ]
train_outputs = outputs[train_idx]
valid_inputs = [inputs[i][valid_idx] for i in range(len(inputs)) ]
valid_outputs = outputs[valid_idx]
K.clear_session()
model = create_model()
optimizer = tf.keras.optimizers.Adam(learning_rate=2e-5)
 model.compile(loss='binary_crossentropy', optimizer=optimizer)
 model.fit(train_inputs, train_outputs, epochs=3, batch_size=6)
 valid_preds.append(model.predict(valid_inputs))
test_preds.append(model.predict(test_inputs))
rho_val = compute_spearmanr_ignore_nan(valid_outputs, valid_preds[-1])
 print('validation score = ', rho_val )<save_to_csv>
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
 test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
 X_train = train.iloc[:, 1:].values.astype('float32')
 y_train = train.iloc[:, 0].values.astype('int32')
 X_test = test.values.astype('float32')
 X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
 X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
 print(X_train.shape, X_test.shape )
df_sub.iloc[:, 1:] = np.average(test_preds, axis=0)
 df_sub.to_csv('submission.csv', index=False )<set_options>
X_train = X_train / 255.0
X_test = X_test / 255.0
py.init_notebook_mode(connected=True)
 pd.set_option('max_columns', None)
 warnings.filterwarnings("ignore" )<load_from_csv>
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=42)
 X_train.shape, X_val.shape, y_train.shape, y_val.shape
train = pd.read_csv('.. /input/tmdb-box-office-prediction/train.csv')
 test = pd.read_csv('.. /input/tmdb-box-office-prediction/test.csv')
 dict_columns = ['belongs_to_collection','genres','spoken_languages','production_companies',
'production_countries','Keywords','cast','crew']
def text_to_dict(df):
for columns in dict_columns:
df[columns] = df[columns].apply(lambda x: {} if pd.isna(x)else ast.literal_eval(x))
return df
train = text_to_dict(train)
 test = text_to_dict(test)
 test['revenue'] = np.nan
train = pd.merge(train, pd.read_csv('.. /input/tmdb-competition-additional-features/TrainAdditionalFeatures.csv'), how='left', on=['imdb_id'])
 test = pd.merge(test, pd.read_csv('.. /input/tmdb-competition-additional-features/TestAdditionalFeatures.csv'), how='left', on=['imdb_id'])
 train.head(2 )<feature_engineering>
datagen = ImageDataGenerator(
 rotation_range=8,
width_shift_range=0.08,
shear_range=0.3,
height_shift_range=0.08,
zoom_range=0.08
)
train.loc[train['id'] == 16,'revenue'] = 192864
train.loc[train['id'] == 90,'budget'] = 30000000
train.loc[train['id'] == 118,'budget'] = 60000000
train.loc[train['id'] == 149,'budget'] = 18000000
train.loc[train['id'] == 313,'revenue'] = 12000000
train.loc[train['id'] == 451,'revenue'] = 12000000
train.loc[train['id'] == 464,'budget'] = 20000000
train.loc[train['id'] == 470,'budget'] = 13000000
train.loc[train['id'] == 513,'budget'] = 930000
train.loc[train['id'] == 797,'budget'] = 8000000
train.loc[train['id'] == 819,'budget'] = 90000000
train.loc[train['id'] == 850,'budget'] = 90000000
train.loc[train['id'] == 1007,'budget'] = 2
train.loc[train['id'] == 1112,'budget'] = 7500000
train.loc[train['id'] == 1131,'budget'] = 4300000
train.loc[train['id'] == 1359,'budget'] = 10000000
train.loc[train['id'] == 1542,'budget'] = 1
train.loc[train['id'] == 1570,'budget'] = 15800000
train.loc[train['id'] == 1571,'budget'] = 4000000
train.loc[train['id'] == 1714,'budget'] = 46000000
train.loc[train['id'] == 1721,'budget'] = 17500000
train.loc[train['id'] == 1865,'revenue'] = 25000000
train.loc[train['id'] == 1885,'budget'] = 12
train.loc[train['id'] == 2091,'budget'] = 10
train.loc[train['id'] == 2268,'budget'] = 17500000
train.loc[train['id'] == 2491,'budget'] = 6
train.loc[train['id'] == 2602,'budget'] = 31000000
train.loc[train['id'] == 2612,'budget'] = 15000000
train.loc[train['id'] == 2696,'budget'] = 10000000
train.loc[train['id'] == 2801,'budget'] = 10000000
train.loc[train['id'] == 335,'budget'] = 2
train.loc[train['id'] == 348,'budget'] = 12
train.loc[train['id'] == 470,'budget'] = 13000000
train.loc[train['id'] == 513,'budget'] = 1100000
train.loc[train['id'] == 640,'budget'] = 6
train.loc[train['id'] == 696,'budget'] = 1
train.loc[train['id'] == 797,'budget'] = 8000000
train.loc[train['id'] == 850,'budget'] = 1500000
train.loc[train['id'] == 1199,'budget'] = 5
train.loc[train['id'] == 1282,'budget'] = 9
train.loc[train['id'] == 1347,'budget'] = 1
train.loc[train['id'] == 1755,'budget'] = 2
train.loc[train['id'] == 1801,'budget'] = 5
train.loc[train['id'] == 1918,'budget'] = 592
train.loc[train['id'] == 2033,'budget'] = 4
train.loc[train['id'] == 2118,'budget'] = 344
train.loc[train['id'] == 2252,'budget'] = 130
train.loc[train['id'] == 2256,'budget'] = 1
train.loc[train['id'] == 2696,'budget'] = 10000000
test.loc[test['id'] == 6733,'budget'] = 5000000
test.loc[test['id'] == 3889,'budget'] = 15000000
test.loc[test['id'] == 6683,'budget'] = 50000000
test.loc[test['id'] == 5704,'budget'] = 4300000
test.loc[test['id'] == 6109,'budget'] = 281756
test.loc[test['id'] == 7242,'budget'] = 10000000
test.loc[test['id'] == 7021,'budget'] = 17540562
test.loc[test['id'] == 5591,'budget'] = 4000000
test.loc[test['id'] == 4282,'budget'] = 20000000
test.loc[test['id'] == 3033,'budget'] = 250
test.loc[test['id'] == 3051,'budget'] = 50
test.loc[test['id'] == 3084,'budget'] = 337
test.loc[test['id'] == 3224,'budget'] = 4
test.loc[test['id'] == 3594,'budget'] = 25
test.loc[test['id'] == 3619,'budget'] = 500
test.loc[test['id'] == 3831,'budget'] = 3
test.loc[test['id'] == 3935,'budget'] = 500
test.loc[test['id'] == 4049,'budget'] = 995946
test.loc[test['id'] == 4424,'budget'] = 3
test.loc[test['id'] == 4460,'budget'] = 8
test.loc[test['id'] == 4555,'budget'] = 1200000
test.loc[test['id'] == 4624,'budget'] = 30
test.loc[test['id'] == 4645,'budget'] = 500
test.loc[test['id'] == 4709,'budget'] = 450
test.loc[test['id'] == 4839,'budget'] = 7
test.loc[test['id'] == 3125,'budget'] = 25
test.loc[test['id'] == 3142,'budget'] = 1
test.loc[test['id'] == 3201,'budget'] = 450
test.loc[test['id'] == 3222,'budget'] = 6
test.loc[test['id'] == 3545,'budget'] = 38
test.loc[test['id'] == 3670,'budget'] = 18
test.loc[test['id'] == 3792,'budget'] = 19
test.loc[test['id'] == 3881,'budget'] = 7
test.loc[test['id'] == 3969,'budget'] = 400
test.loc[test['id'] == 4196,'budget'] = 6
test.loc[test['id'] == 4221,'budget'] = 11
test.loc[test['id'] == 4222,'budget'] = 500
test.loc[test['id'] == 4285,'budget'] = 11
test.loc[test['id'] == 4319,'budget'] = 1
test.loc[test['id'] == 4639,'budget'] = 10
test.loc[test['id'] == 4719,'budget'] = 45
test.loc[test['id'] == 4822,'budget'] = 22
test.loc[test['id'] == 4829,'budget'] = 20
test.loc[test['id'] == 4969,'budget'] = 20
test.loc[test['id'] == 5021,'budget'] = 40
test.loc[test['id'] == 5035,'budget'] = 1
test.loc[test['id'] == 5063,'budget'] = 14
test.loc[test['id'] == 5119,'budget'] = 2
test.loc[test['id'] == 5214,'budget'] = 30
test.loc[test['id'] == 5221,'budget'] = 50
test.loc[test['id'] == 4903,'budget'] = 15
test.loc[test['id'] == 4983,'budget'] = 3
test.loc[test['id'] == 5102,'budget'] = 28
test.loc[test['id'] == 5217,'budget'] = 75
test.loc[test['id'] == 5224,'budget'] = 3
test.loc[test['id'] == 5469,'budget'] = 20
test.loc[test['id'] == 5840,'budget'] = 1
test.loc[test['id'] == 5960,'budget'] = 30
test.loc[test['id'] == 6506,'budget'] = 11
test.loc[test['id'] == 6553,'budget'] = 280
test.loc[test['id'] == 6561,'budget'] = 7
test.loc[test['id'] == 6582,'budget'] = 218
test.loc[test['id'] == 6638,'budget'] = 5
test.loc[test['id'] == 6749,'budget'] = 8
test.loc[test['id'] == 6759,'budget'] = 50
test.loc[test['id'] == 6856,'budget'] = 10
test.loc[test['id'] == 6858,'budget'] = 100
test.loc[test['id'] == 6876,'budget'] = 250
test.loc[test['id'] == 6972,'budget'] = 1
test.loc[test['id'] == 7079,'budget'] = 8000000
test.loc[test['id'] == 7150,'budget'] = 118
test.loc[test['id'] == 6506,'budget'] = 118
test.loc[test['id'] == 7225,'budget'] = 6
test.loc[test['id'] == 7231,'budget'] = 85
test.loc[test['id'] == 5222,'budget'] = 5
test.loc[test['id'] == 5322,'budget'] = 90
test.loc[test['id'] == 5350,'budget'] = 70
test.loc[test['id'] == 5378,'budget'] = 10
test.loc[test['id'] == 5545,'budget'] = 80
test.loc[test['id'] == 5810,'budget'] = 8
test.loc[test['id'] == 5926,'budget'] = 300
test.loc[test['id'] == 5927,'budget'] = 4
test.loc[test['id'] == 5986,'budget'] = 1
test.loc[test['id'] == 6053,'budget'] = 20
test.loc[test['id'] == 6104,'budget'] = 1
test.loc[test['id'] == 6130,'budget'] = 30
test.loc[test['id'] == 6301,'budget'] = 150
test.loc[test['id'] == 6276,'budget'] = 100
test.loc[test['id'] == 6473,'budget'] = 100
test.loc[test['id'] == 6842,'budget'] = 30 
<feature_engineering>
learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy',
patience=3,
verbose=1,
factor=0.5,
min_lr=0.00001 )
train['popularity'] = np.log1p(train['popularity'])
 train['revenue'] = np.log1p(train['revenue'])
 train['totalVotes'] = np.log1p(train['totalVotes'])
 train['budget'] = np.log1p(train['budget'])
 train['runtime'] = np.log1p(train['runtime'])
 train['popularity2'] = np.log1p(train['popularity2'])
 test['popularity'] = np.log1p(test['popularity'])
 test['totalVotes'] = np.log1p(test['totalVotes'])
 test['budget'] = np.log1p(test['budget'])
 test['runtime'] = np.log1p(test['runtime'])
 test['popularity2'] = np.log1p(test['popularity2'] )<define_variables>
from keras.models import Sequential
from keras.layers import Dense, Flatten, Dropout, Conv2D, MaxPool2D, BatchNormalization
for i,e in enumerate(train['belongs_to_collection'][:2]):
print(i,e )<count_values>
model = Sequential()
model.add(Conv2D(32, kernel_size=3, activation='relu', input_shape=(28, 28, 1)))
 model.add(BatchNormalization())
 model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
 model.add(Conv2D(32, kernel_size=5, strides=2, padding='same', activation='relu'))
model.add(BatchNormalization())
 model.add(Dropout(0.4))
model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
 model.add(Conv2D(64, kernel_size=3, activation='relu'))
model.add(BatchNormalization())
 model.add(Conv2D(64, kernel_size=5, strides=2, padding='same', activation='relu'))
model.add(BatchNormalization())
 model.add(Dropout(0.4))
model.add(Flatten())
 model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
 model.add(Dropout(0.4))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'] )
train['belongs_to_collection'].apply(lambda x: 1 if x!= {} else 0 ).value_counts()<feature_engineering>
epochs = 30
batch_size = 64
history = model.fit_generator(generator=datagen.flow(X_train, y_train),
steps_per_epoch=X_train.shape[0] // batch_size,
epochs=epochs,
validation_data=datagen.flow(X_val, y_val),
validation_steps=X_val.shape[0] // batch_size )
<define_variables><EOS> 
predictions = model.predict_classes(X_test, verbose=0)
 submissions = pd.DataFrame({'ImageID': list(range(1, len(predictions)+ 1)) ,
'Label': predictions})
 submissions.to_csv('cnn_part3.csv', index=False, header=True )