gistfile1.txt

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


sns.set(style='white', context='notebook', palette='deep')

# 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)

# free some space
del train

g = sns.countplot(Y_train)

print(Y_train.value_counts())

print()
print("Checking for missing values in the training set:")
print(X_train.isnull().any().describe())

print()
print("and, in the test set:")
print(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 , channels = 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 architechture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out

model = Sequential()

# Layer 1:  32 3x3 convolutions (x2)
model.add(Conv2D(filters     = 32,
                 kernel_size = (3,3),
                 padding     = 'Same',
                 activation  ='relu',
                 input_shape = (28,28,1)))

model.add(Conv2D(filters     = 32,
                 kernel_size = (3,3),
                 padding     = 'Same',
                 activation  ='relu'))

model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.20))

# Layer 2 CNN 64 3x3 convolutions (X2)
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(0.20))

# Layer 3 CNN
model.add(Conv2D(filters     = 128,
                 kernel_size = (3,3),
                 padding     = 'Same',
                 activation  ='relu'))

model.add(Conv2D(filters     = 128,
                 kernel_size = (3,3),
                 padding     = 'Same',
                 activation  ='relu'))
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.20))

# Layer 4:  FC, Softmax output
model.add(Flatten())
model.add(Dense(512, activation = "relu"))
model.add(Dropout(0.25))
model.add(Dense(256, activation = "relu"))
model.add(Dropout(0.25))
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   = 0.8,
                                            min_lr   = 0.000001)

epochs     = 250
batch_size = 512

datagen = ImageDataGenerator(
        featurewise_center            = False, # set input mean to 0 over the 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                = 12,    # randomly rotate images in the range (degrees, 0 to 180)
        zoom_range                    = 0.10,  # Randomly zoom image
        width_shift_range             = 0.15,  # randomly shift images horizontally (fraction of total width)
        height_shift_range            = 0.15,  # 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         = 1,
                              steps_per_epoch = X_train.shape[0] // batch_size,
                              callbacks       = [learning_rate_reduction])

#==========================================================================================

# Predict the values 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)

# 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 index 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("my_kaggle_mnist_submission.csv",index=False)