classification_08_06.py

# -*- coding: utf-8 -*-
"""
Created on Tue Jun  8 16:50:19 2021

@author: Alkios
"""

import numpy as np

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, Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ReduceLROnPlateau





Xtrain, Ytrain = Xt, Yt

Xtrain = Xtrain / 255.0

Xtrain = Xtrain.reshape(-1,128,128,1)

Ytrain = to_categorical(Ytrain, num_classes = 7)





random_seed = 2

# Split the train and the validation set for the fitting
Xtrain, Xval, Ytrain, Yval = train_test_split(Xtrain, Ytrain, test_size = 0.3, random_state=random_seed)

# Set the CNN model 
# my CNN architechture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out

model = Sequential()

model.add(Conv2D(filters = 16, kernel_size = (5,5),padding = 'Same', 
                 activation ='relu', input_shape = (128,128,1)))

model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(filters = 16, kernel_size = (5,5),padding = 'Same', 
                 activation ='relu'))

model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.25))


model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', 
                 activation ='relu'))

model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(filters = 32, kernel_size = (5,5),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(128, activation = "relu"))
model.add(Dropout(0.1))

model.add(Dense(7, activation = "softmax"))


#optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)


optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999)


model.summary()
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 = 300 # Turn epochs to 30 to get 0.9967 accuracy
batch_size = 64





# With data augmentation to prevent overfitting (accuracy 0.99286)

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=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(Xtrain)





#history = model.fit_generator(datagen.flow(Xtrain,Ytrain, batch_size=batch_size),
                              #epochs = epochs, validation_data = (Xval,Yval),
                              #verbose = 2, steps_per_epoch=Xtrain.shape[0] // batch_size
                              #, callbacks=[learning_rate_reduction])



#history = model.fit_generator(datagen.flow(Xtrain,Ytrain, batch_size=batch_size),
                              #epochs = epochs, validation_data = (Xval,Yval),
                              #verbose = 2, steps_per_epoch=Xtrain.shape[0] // batch_size)


# Fit the model
# Without data augmentation i obtained an accuracy of 0.98114
history = model.fit(Xtrain, Ytrain, batch_size = batch_size, epochs = epochs, 
          validation_data = (Xval, Yval), verbose = 2)


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










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 Stokes number')
    plt.xlabel('Predicted Stokes number')


classesd = []
for i in d:
    classesd.append(i[2:])

# Predict the values from the validation dataset
Y_pred = model.predict(Xval)
# 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(Yval,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 = classesd) 


# 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 = Xval[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, figsize=(8,8))
    for row in range(nrows):
        for col in range(ncols):
            error = errors_index[n]
            ax[row,col].imshow((img_errors[error]).reshape((128,128)), cmap = 'hot')
            ax[row,col].set_title("Predicted Stokes nb :{}\nTrue Stokes nb :{}".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:]


numbers1 = [e[ni] for ni in Y_pred_classes_errors]
numbers2 = [e[ni] for ni in Y_true_errors]
# Show the top 6 errors
display_errors(most_important_errors, X_val_errors, numbers1, numbers2)