Kaggle competition: PadhAI: Text - Non Text Classification Level 4a
<SOS> metric: categorizationaccuracy Kaggle data source: padhai:-text-non-text-classification-level-4a<choose_model_class>
sns.set(style='white')
 np.seterr(all='ignore')
 np.random.seed(100)
 LEVEL = 'level_4a'
model_sol_5.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'] )<train_model>
class SigmoidNeuron:
def __init__(self):
self.w = None
self.b = None
def perceptron(self, x):
return np.dot(x, self.w.T)+ self.b
def sigmoid(self, x):
return 1.0/(1.0 + np.exp(-x))
def grad_w_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)* x
def grad_b_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)
 def grad_w_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred * x
elif y == 1:
return -1 *(1 - y_pred)* x
else:
raise ValueError("y should be 0 or 1")
 def grad_b_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred
elif y == 1:
return -1 *(1 - y_pred)
 else:
raise ValueError("y should be 0 or 1")
 def fit(self, X, Y, epochs=1, learning_rate=1, initialise=True, loss_fn="mse", display_loss=False):
if initialise:
self.w = np.random.randn(1, X.shape[1])
 self.b = 0
if display_loss:
loss = {}
for i in tqdm_notebook(range(epochs), total=epochs, unit="epoch"):
dw = 0
db = 0
for x, y in zip(X, Y):
if loss_fn == "mse":
dw += self.grad_w_mse(x, y)
 db += self.grad_b_mse(x, y)
 elif loss_fn == "ce":
dw += self.grad_w_ce(x, y)
 db += self.grad_b_ce(x, y)
 self.w -= learning_rate * dw
self.b -= learning_rate * db
if display_loss:
Y_pred = self.sigmoid(self.perceptron(X))
if loss_fn == "mse":
loss[i] = mean_squared_error(Y, Y_pred)
 elif loss_fn == "ce":
loss[i] = log_loss(Y, Y_pred)
 if display_loss:
plt.plot(loss.values())
 plt.xlabel('Epochs')
 if loss_fn == "mse":
plt.ylabel('Mean Squared Error')
 elif loss_fn == "ce":
plt.ylabel('Log Loss')
 plt.show()
def predict(self, X):
Y_pred = []
for x in X:
y_pred = self.sigmoid(self.perceptron(x))
Y_pred.append(y_pred)
 return np.array(Y_pred )
checkpointer = ModelCheckpoint(filepath='mnist.model.best.hdf5', verbose=1, save_best_only=True)
 hist_sol_5 = model_sol_5.fit_generator(generator=train_batches_sol5, steps_per_epoch=s5_train_x.shape[0] // 64,
epochs=32, callbacks=[checkpointer],
validation_data=val_batches_sol5, validation_steps=s5_test_x.shape[0] // 64, verbose=2 )<train_model>
languages = ['ta', 'hi', 'en']
images_train = read_all(".. /input/level_4a_train/"+LEVEL+"/"+"background", key_prefix='bgr_')
 for language in languages:
images_train.update(read_all(".. /input/level_4a_train/"+LEVEL+"/"+language, key_prefix=language+"_"))
print(len(images_train))
images_test = read_all(".. /input/level_4a_test/kaggle_"+LEVEL, key_prefix='')
 print(len(images_test))
model_sol_5.load_weights('mnist.model.best.hdf5')
 score = model_sol_5.evaluate(s5_test_x, ohe_s5_test_y, verbose=0)
 accuracy = 100 * score[1]
print('Test accuracy: %.4f%%' % accuracy )<save_to_csv>
scaler = StandardScaler()
X_scaled_train = scaler.fit_transform(X_train)
 X_scaled_test = scaler.transform(X_test )
predictions = model_sol_5.predict(test_x_sol5)
 predictions = [ np.argmax(x)for x in predictions ]
submission = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
 submission.drop('Label', axis=1, inplace=True)
 submission['Label'] = predictions
submission.to_csv('submission5.csv', index=False )<choose_model_class>
sn_mse = SigmoidNeuron()
sn_mse.fit(X_scaled_train, Y_train, epochs=200, learning_rate=0.01, loss_fn="mse", display_loss=True )
model_sol_6 = Sequential()
model_sol_6.add(Lambda(standardize, input_shape=(28,28,1)))
 model_sol_6.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))
model_sol_6.add(BatchNormalization())
 model_sol_6.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))
model_sol_6.add(MaxPooling2D(pool_size=2))
model_sol_6.add(Dropout(0.1))
model_sol_6.add(BatchNormalization())
 model_sol_6.add(Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'))
model_sol_6.add(MaxPooling2D(pool_size=2))
model_sol_6.add(Flatten())
 model_sol_6.add(BatchNormalization())
 model_sol_6.add(Dense(64))
model_sol_6.add(Activation('relu'))
model_sol_6.add(Dropout(0.2))
model_sol_6.add(BatchNormalization())
 model_sol_6.add(Dense(10, activation='softmax')) 
<choose_model_class>
sn_ce = SigmoidNeuron()
sn_ce.fit(X_scaled_train, Y_train, epochs=200, learning_rate=0.01, loss_fn="ce", display_loss=True )
model_sol_6.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'] )<train_model>
def print_accuracy(sn):
Y_pred_train = sn.predict(X_scaled_train)
 Y_pred_binarised_train =(Y_pred_train >= 0.5 ).astype("int" ).ravel()
accuracy_train = accuracy_score(Y_pred_binarised_train, Y_train)
 print("Train Accuracy : ", accuracy_train)
 print("-"*50 )
checkpointer = ModelCheckpoint(filepath='mnist.model.best.hdf5', verbose=1, save_best_only=True)
 hist_sol_6 = model_sol_6.fit_generator(generator=train_batches_sol5, steps_per_epoch=s5_train_x.shape[0] // 64,
epochs=32, callbacks=[checkpointer],
validation_data=val_batches_sol5, validation_steps=s5_test_x.shape[0] // 64, verbose=2 )<train_model>
print_accuracy(sn_ce )
model_sol_6.load_weights('mnist.model.best.hdf5')
 score = model_sol_6.evaluate(s5_test_x, ohe_s5_test_y, verbose=0)
 accuracy = 100 * score[1]
print('Test accuracy: %.4f%%' % accuracy )<train_model>
Y_pred_test = sn_ce.predict(X_scaled_test)
 Y_pred_binarised_test =(Y_pred_test >= 0.5 ).astype("int" ).ravel()
submission = {}
submission['ImageId'] = ID_test
submission['Class'] = Y_pred_binarised_test
submission = pd.DataFrame(submission)
 submission = submission[['ImageId', 'Class']]
submission = submission.sort_values(['ImageId'])
 submission.to_csv("submisision.csv", index=False )