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# prediction
results = model.predict(test)

# Index with the maximum probability
results = np.argmax(results,axis = 1)
results = pd.Series(results,name="Label")

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datagen = ImageDataGenerator(
        featurewise_center=False,  
        samplewise_center=False,  
        featurewise_std_normalization=False,  
        samplewise_std_normalization=False,  
        zca_whitening=False,  
        rotation_range=10,  # randomly rotate images
        zoom_range = 0.2, # Randomly zoom image 
        width_shift_range=0.2,  # randomly shift images horizontally
        height_shift_range=0.2,  # randomly shift images vertically

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# Set LR metric monitor
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', 
                                            patience=3, 
                                            verbose=1, 
                                            factor=0.4, 
                                            min_lr=0.00001)
epochs = 500 
batch_size = 32 #to start with 

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# Optimizer
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
# Compile the model
model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"])

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model = Sequential()

model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', 
                 activation ='relu', input_shape = (28,28,1)))
model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', 
                 activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', 

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# random seed
random_seed = 3
# Split
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.2, random_state=random_seed)

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Y_train = to_categorical(Y_train, num_classes = 10)

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#Normalize
X_train = X_train / 255.0
test = test / 255.0
#Reshape
X_train = X_train.values.reshape(-1,28,28,1)
test = test.values.reshape(-1,28,28,1)

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X_train.isnull().any().describe()
test.isnull().any().describe()

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# load data and visualize the histogram
train = pd.read_csv("../input/train.csv")
test = pd.read_csv("../input/test.csv")
Y_train = train["label"]
X_train = train.drop(labels = ["label"],axis = 1) 
g = sns.countplot(Y_train)
Y_train.value_counts()