Kaggle---DigitRecognizer（数字识别）

Introduction to CNN Keras - 0.997 (top 10%)

数字识别这是kaggle上入门的一道经典题目，最近准备做计算机视觉，所以也是学习了一下，代码供参考。最后取得了0。997的成绩。首次进入kaggle的Top 10%。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import seaborn as sns

np.random.seed(2)
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import itertools
from keras.utils.np_utils import to_categorical #convert to one-hot-encoding
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.optimizers import RMSprop
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ReduceLROnPlateau

# Load the data
train = pd.read_csv("./data/train.csv")
test = pd.read_csv("./data/test.csv")
Y_train = train["label"]
# Drop 'label' column
X_train = train.drop(labels = ['label'],axis=1)
del train
g = sns.countplot(Y_train)
Y_train.value_counts()
# Check the data
X_train.isnull().any().describe()
test.isnull().any().describe()
# Normalize the data
X_train = X_train / 255.0
test = test / 255.0
# Reshape image in 3 dimensions (height = 28px, width = 28px, canal = 1)
X_train = X_train.values.reshape(-1, 28, 28, 1)
test = test.values.reshape(-1, 28, 28, 1)
# Encode labels to one hot vectors (ex: 2 -> [0,0,1,0,0,0,0,0,0,0])
Y_train = to_categorical(Y_train, num_classes=10)
# Set the random seed
random_seed = 2
# Split the train and the validation set for the fitting
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=random_seed)
# Some examples
g = plt.imshow(X_train[0][:,:,0])


# Set the CNN model
# my CNN architecture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout ->Out
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',activation='relu'))
model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same',activation='relu'))
model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(256, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation = 'softmax'))

# Define the 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"])

# Set a learning rate annealer
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
                                           patience=3,
                                           verbose=1,
                                           factor=0.5,
                                           min_lr=0.00001)

epochs = 30 # True epochs to 30 to get 0.9967 accuracy
batch_size = 86

# With data augmentation to prevent overfitting (accuracy 0.99286)
datagen = ImageDataGenerator(
        featurewise_center=False, # set input mean to 0 over dataset
        samplewise_center=False, # set each sample mean to 0
        featurewise_std_normalization=False,  # divide inputs by std of the dataset
        samplewise_std_normalization=False,  # divide each input by its std
        zca_whitening=False,  # apply ZCA whitening
        rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)
        zoom_range = 0.1, # Randomly zoom image 
        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
        horizontal_flip=False,  # randomly flip images
        vertical_flip=False)  # randomly flip images)

datagen.fit(X_train)

# Fit the model
history = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size),
                             epochs = epochs, validation_data = (X_val, Y_val),
                             verbose = 2, steps_per_epoch=X_train.shape[0] //batch_size
                             ,callbacks=[learning_rate_reduction])

# Plot the loss and accuracy curves for training and validation
fig, ax = plt.subplots(2,1)
ax[0].plot(history.history['loss'], color='b', label = "Training loss")
ax[0].plot(history.history['val_loss'], color='r', label = "validation loss",axes=ax[0])
legend = ax[0].legend(loc='best', shadow=True)

ax[1].plot(history.history['acc'], color='b', label = "Training accuracy")
ax[1].plot(history.history['val_acc'], color='r', label = "Validation accuracy")
legend = ax[1].legend(loc='best', shadow=True)

# Look at confusion matrix
def plot_confusion_matrix(cm, classes,
                         normalize=False,
                         title='Confusion matrix',
                         cmap=plt.cm.Blues):
    '''
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`
    '''
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:,np.newaxis]

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                horizontalalignment='center',
                color='white' if cm[i,j] > thresh else 'black')
        plt.tight_layout()
        plt.ylabel('True label')
        plt.xlabel('Predicted label')

# Predict the value from the validation dataset
Y_pred = model.predict(X_val)
# Convert predictions classes to one hot vectors
Y_pred_classes = np.argmax(Y_pred, axis=1)
# Convert validation observations to one hot vectors
Y_true = np.argmax(Y_val, axis=1)
# compute the confusion matrix
confusion_mtx = confusion_matrix(Y_true, Y_pred_classes)
# plot the confusion matrix
plot_confusion_matrix(confusion_mtx, classes=range(10))

# Display some error results

#Errors are difference between predicted labels and true labels
errors = (Y_pred_classes - Y_true != 0)

Y_pred_classes_errors = Y_pred_classes[errors]
Y_pred_errors = Y_pred[errors]
Y_true_errors = Y_true[errors]
X_val_errors = X_val[errors]

def display_errors(errors_index, img_errors,pred_errors, obs_errors):
    '''This function shows 6 images with their predicted and real labels'''
    n = 0
    nrows = 2
    ncols = 3
    fig, ax = plt.subplots(nrows,ncols,sharex=True,sharey=True)
    for row in range(nrows):
        for col in range(ncols):
            error = errors_index[n]
            ax[row,col].imshow((img_errors[error]).reshape((28,28)))
            ax[row,col].set_title("Predicted label :{}\nTrue label :{}".format(pred_errors[error],obs_errors[error]))
            n += 1

# Probabilities of the wrong predicted numbers
Y_pred_errors_prob = np.max(Y_pred_errors,axis = 1)

# Predicted probabilities of the true values in the error set
true_prob_errors = np.diagonal(np.take(Y_pred_errors, Y_true_errors, axis=1))

# Difference between the probability of the predicted label and the true label
delta_pred_true_errors = Y_pred_errors_prob - true_prob_errors

# Sorted list of the delta prob errors
sorted_dela_errors = np.argsort(delta_pred_true_errors)

# Top 6 errors
most_important_errors = sorted_dela_errors[-6:]

# Show the top 6 errors
display_errors(most_important_errors, X_val_errors, Y_pred_classes_errors, Y_true_errors)

# Predict results
results = model.predict(test)

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

submission = pd.concat([pd.Series(range(1,28001),name = "ImageId"),results],axis = 1)
submission.to_csv("cnn_mnist_datagen.csv",index=False)