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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Importing Necessary Modules
To discover something new is to explore where it has never been explored.
Added Conv  Visuals Also (Working)
Step1:  Loading The Dataset
Step2:  Normalising The Data
Step3:  Printing the shape of the Datasets
Step4:  ## Reshape To Match The Keras's Expectations
Step5:  Linear Model
Step8:  Basic Simple Plot And Evaluation
Step9:  Activations Look Like What?
It looks like diversity of the similar patterns present on multiple classes effect the performance of the classifier although CNN is a robust architechture.
Step10:  Let's see the activation of the 2nd channel of the first layer
Step11:  Let's plot the activations of the other conv layers as well.
Step12:  Classifcation Report
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Python Code:
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt #for plotting
from collections import Counter
from sklearn.metrics import confusion_matrix
import itertools
import seaborn as sns
from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))
%matplotlib inline
Explanation: Importing Necessary Modules
To discover something new is to explore where it has never been explored.
Added Conv  Visuals Also (Working)
End of explanation
#loading the dataset.......(Train)
train = pd.read_csv("../input/train.csv")
print(train.shape)
train.head()
z_train = Counter(train['label'])
z_train
sns.countplot(train['label'])
#loading the dataset.......(Test)
test= pd.read_csv("../input/test.csv")
print(test.shape)
test.head()
x_train = (train.ix[:,1:].values).astype('float32') # all pixel values
y_train = train.ix[:,0].values.astype('int32') # only labels i.e targets digits
x_test = test.values.astype('float32')
%matplotlib inline
# preview the images first
plt.figure(figsize=(12,10))
x, y = 10, 4
for i in range(40):  
    plt.subplot(y, x, i+1)
    plt.imshow(x_train[i].reshape((28,28)),interpolation='nearest')
plt.show()
Explanation: Loading The Dataset
End of explanation
x_train = x_train/255.0
x_test = x_test/255.0
y_train
Explanation: Normalising The Data
End of explanation
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
Explanation: Printing the shape of the Datasets
End of explanation
X_train = x_train.reshape(x_train.shape[0], 28, 28,1)
X_test = x_test.reshape(x_test.shape[0], 28, 28,1)
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ReduceLROnPlateau
from sklearn.model_selection import train_test_split
batch_size = 64
num_classes = 10
epochs = 20
input_shape = (28, 28, 1)
# convert class vectors to binary class matrices One Hot Encoding
y_train = keras.utils.to_categorical(y_train, num_classes)
X_train, X_val, Y_train, Y_val = train_test_split(X_train, y_train, test_size = 0.1, random_state=42)
Explanation: ## Reshape To Match The Keras's Expectations
End of explanation
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',kernel_initializer='he_normal',input_shape=input_shape))
model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',kernel_initializer='he_normal'))
model.add(MaxPool2D((2, 2)))
model.add(Dropout(0.20))
model.add(Conv2D(64, (3, 3), activation='relu',padding='same',kernel_initializer='he_normal'))
model.add(Conv2D(64, (3, 3), activation='relu',padding='same',kernel_initializer='he_normal'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), activation='relu',padding='same',kernel_initializer='he_normal'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.25))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.RMSprop(),
              metrics=['accuracy'])
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', 
                                            patience=3, 
                                            verbose=1, 
                                            factor=0.5, 
                                            min_lr=0.0001)
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=15, # 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
model.summary()
datagen.fit(X_train)
h = 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],)
Explanation: Linear Model
End of explanation
final_loss, final_acc = model.evaluate(X_val, Y_val, verbose=0)
print("Final loss: {0:.6f}, final accuracy: {1:.6f}".format(final_loss, final_acc))
# Look at confusion matrix 
#Note, this code is taken straight from the SKLEARN website, an nice way of viewing 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 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) 
# 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))
print(h.history.keys())
accuracy = h.history['acc']
val_accuracy = h.history['val_acc']
loss = h.history['loss']
val_loss = h.history['val_loss']
epochs = range(len(accuracy))
plt.plot(epochs, accuracy, 'bo', label='Training accuracy')
plt.plot(epochs, val_accuracy, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
# 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)
Explanation: Basic Simple Plot And Evaluation
End of explanation
test_im = X_train[154]
plt.imshow(test_im.reshape(28,28), cmap='viridis', interpolation='none')
Explanation: Activations Look Like What?
It looks like diversity of the similar patterns present on multiple classes effect the performance of the classifier although CNN is a robust architechture.
End of explanation
from keras import models
layer_outputs = [layer.output for layer in model.layers[:8]]
activation_model = models.Model(input=model.input, output=layer_outputs)
activations = activation_model.predict(test_im.reshape(1,28,28,1))
first_layer_activation = activations[0]
plt.matshow(first_layer_activation[0, :, :, 4], cmap='viridis')
Explanation: Let's see the activation of the 2nd channel of the first layer:
Had taken help from the keras docs, this answer on  StackOverFlow
End of explanation
model.layers[:-1]# Droping The Last Dense Layer
layer_names = []
for layer in model.layers[:-1]:
    layer_names.append(layer.name) 
images_per_row = 16
for layer_name, layer_activation in zip(layer_names, activations):
    if layer_name.startswith('conv'):
        n_features = layer_activation.shape[-1]
        size = layer_activation.shape[1]
        n_cols = n_features // images_per_row
        display_grid = np.zeros((size * n_cols, images_per_row * size))
        for col in range(n_cols):
            for row in range(images_per_row):
                channel_image = layer_activation[0,:, :, col * images_per_row + row]
                channel_image -= channel_image.mean()
                channel_image /= channel_image.std()
                channel_image *= 64
                channel_image += 128
                channel_image = np.clip(channel_image, 0, 255).astype('uint8')
                display_grid[col * size : (col + 1) * size,
                             row * size : (row + 1) * size] = channel_image
        scale = 1. / size
        plt.figure(figsize=(scale * display_grid.shape[1],
                            scale * display_grid.shape[0]))
        plt.title(layer_name)
        plt.grid(False)
        plt.imshow(display_grid, aspect='auto', cmap='viridis')
layer_names = []
for layer in model.layers[:-1]:
    layer_names.append(layer.name) 
images_per_row = 16
for layer_name, layer_activation in zip(layer_names, activations):
    if layer_name.startswith('max'):
        n_features = layer_activation.shape[-1]
        size = layer_activation.shape[1]
        n_cols = n_features // images_per_row
        display_grid = np.zeros((size * n_cols, images_per_row * size))
        for col in range(n_cols):
            for row in range(images_per_row):
                channel_image = layer_activation[0,:, :, col * images_per_row + row]
                channel_image -= channel_image.mean()
                channel_image /= channel_image.std()
                channel_image *= 64
                channel_image += 128
                channel_image = np.clip(channel_image, 0, 255).astype('uint8')
                display_grid[col * size : (col + 1) * size,
                             row * size : (row + 1) * size] = channel_image
        scale = 1. / size
        plt.figure(figsize=(scale * display_grid.shape[1],
                            scale * display_grid.shape[0]))
        plt.title(layer_name)
        plt.grid(False)
        plt.imshow(display_grid, aspect='auto', cmap='viridis')
layer_names = []
for layer in model.layers[:-1]:
    layer_names.append(layer.name) 
images_per_row = 16
for layer_name, layer_activation in zip(layer_names, activations):
    if layer_name.startswith('drop'):
        n_features = layer_activation.shape[-1]
        size = layer_activation.shape[1]
        n_cols = n_features // images_per_row
        display_grid = np.zeros((size * n_cols, images_per_row * size))
        for col in range(n_cols):
            for row in range(images_per_row):
                channel_image = layer_activation[0,:, :, col * images_per_row + row]
                channel_image -= channel_image.mean()
                channel_image /= channel_image.std()
                channel_image *= 64
                channel_image += 128
                channel_image = np.clip(channel_image, 0, 255).astype('uint8')
                display_grid[col * size : (col + 1) * size,
                             row * size : (row + 1) * size] = channel_image
        scale = 1. / size
        plt.figure(figsize=(scale * display_grid.shape[1],
                            scale * display_grid.shape[0]))
        plt.title(layer_name)
        plt.grid(False)
        plt.imshow(display_grid, aspect='auto', cmap='viridis')
Explanation: Let's plot the activations of the other conv layers as well.
End of explanation
#get the predictions for the test data
predicted_classes = model.predict_classes(X_test)
#get the indices to be plotted
y_true = test.iloc[:, 0]
correct = np.nonzero(predicted_classes==y_true)[0]
incorrect = np.nonzero(predicted_classes!=y_true)[0]
from sklearn.metrics import classification_report
target_names = ["Class {}".format(i) for i in range(num_classes)]
print(classification_report(y_true, predicted_classes, target_names=target_names))
submissions=pd.DataFrame({"ImageId": list(range(1,len(predicted_classes)+1)),
                         "Label": predicted_classes})
submissions.to_csv("asd.csv", index=False, header=True)
model.save('my_model_1.h5')
json_string = model.to_json()
Explanation: Classifcation Report
End of explanation
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