MNIST (“Modified National Institute of Standards and Technology”) is the de-facto “hello world” data set of computer vision. Since its release in 1999, this classic data set of handwritten images has served as the basis for bench marking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.
In this exercise, the goal is to correctly identify digits from a data set of tens of thousands of handwritten images.
This Notebook contains three main parts:
-The data preparation
-The CNN modeling and evaluation
-The results prediction and analysis
The Google Drive is mounted on the Google Colab Notebook. The train & test data is then loaded using the well known pandas
read_csv(). The train set 42000 rows and 785 columns( including labels) and the test set has 28000 rows and 784 columns. As known, the test set is not supposed to have the labels.
After importing the data sets, the label column from the train set is saved as another data frame, where as it is dropped from the original train set. This is merely done due to input requirements of the fit() function which takes the data & the labels separately during the model training phase.
Distribution of Training data among Classes
The training data seems to be distributed equally among the classes ranging from 0 - 9.
Checking for Null Data
We checked for null data, but did not find any. No further actions taken in this regard.
We perform a gray scale normalization to reduce the effect of illumination’s differences. Moreover the CNN converge faster when pixel values ranges in [0, 1] rather than on [0, 255].
Train and test images has been stock into pandas dataframe as 1D vectors of 784 values. We reshape all data to (28x28x1) 3D matrices. Keras(with TensorFlow back end) requires an extra dimension in the end which correspond to channels. MNIST images are gray scaled so it use only one channel. Had it been colored or RGB images containing 3 channels each corresponding to Red, Green & Blue, we would have reshaped 784 vectors to (28x28x3) 3D matrices.
Labels are 10 digits numbers from 0 to 9. We need to encode these labels to one hot vectors.
Splitting the Train Set
Next we split the train set into train and test in the ratio of 9:1. This 10% set will act as the validation set, to prevent our model from over fitting on the train set.
CNN Modelling & Evaluation
The first is the convolution (Conv2D) layer. It is like a set of learnable filters. We chose to set 64 filters for the two firsts Conv2D layers and 64 filters for the lat one. Each filter transforms a part of the image as defined by the kernel size(3x3) using the kernel filter. The kernel filter matrix is applied on the whole image. Filters are used to transform the image.
The CNN can isolate features that are useful everywhere from these transformed images (feature maps).
The second important layer in CNN is the pooling (MaxPool2D) layer. This layer simply acts as a down sampling filter. It looks at the 2 neighboring pixels and picks the maximal value. These are used to reduce computational cost, and to some extent also reduces over fitting. We chose the pooling size as (2x2).
Combining convolution and pooling layers, CNN are able to combine local features and learn more global features of the image.
Dropout is a regularization method, where a proportion of nodes in the layer are randomly ignored (setting their weights to zero) for each training sample. This drops randomly a proportion of the network and forces the network to learn features in a distributed way. This technique also improves generalization and reduces the over fitting.
‘relu’ is the rectified linear activation function Mathematically it can be defined as f(x) = max(0,x). The activation function is used to add non linearity to the network.
The Flatten layer is use to convert the final feature maps into a one single 1D vector. This flattening step is needed so that you can make use of fully connected layers after some convolution/maxpool layers. It combines all the found local features of the previous convolution layers.
In the end, two fully-connected (Dense) layers are used which are just artificial an neural networks (ANN). In the last dense layer, ‘softmax’ activation is used which is used to output distribution of probability of each class.
Here is the Sequential model created:
model= tf.keras.models.Sequential( [tf.keras.layers.Conv2D(filters = 64, kernel_size = (3,3), activation = 'relu', input_shape = (28,28,1)), tf.keras.layers.Conv2D(filters = 64, kernel_size = (3,3), activation = 'relu'), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Dropout(.25), tf.keras.layers.Conv2D(filters = 64, kernel_size = (3,3), activation = 'relu'), tf.keras.layers.MaxPooling2D(2,2), tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10, activation='softmax')] )
Loss Function & Optimizer
We choose the loss function as ‘categorical cross entropy’ as the number of categorical classifications is more than 2. The loss function is the error rate between the observed labels and the predicted ones.
We used the Adam Optimizer for this particular problem.
model.summary() Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) (None, 26, 26, 64) 640 _________________________________________________________________ conv2d_1 (Conv2D) (None, 24, 24, 64) 36928 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 12, 12, 64) 0 _________________________________________________________________ dropout (Dropout) (None, 12, 12, 64) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 10, 10, 64) 36928 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64) 0 _________________________________________________________________ flatten (Flatten) (None, 1600) 0 _________________________________________________________________ dense (Dense) (None, 128) 204928 _________________________________________________________________ dense_1 (Dense) (None, 10) 1290 ================================================================= Total params: 280,714 Trainable params: 280,714 Non-trainable params: 0 _________________________________________________________________
Train the model
The fit function is responsible for training the model. It takes as input the train data as x and the labels as y. In addition to this, we use a validation set as discussed earlier. In addition to this choose batch size as 100, epochs = 24 and verbose = 2. We trained the model using the GPU available on Google Colab.
model.fit(X_train, Y_train,validation_data=(X_test, Y_test), batch_size=100, epochs=24, verbose=2)
Result Prediction & Analysis
After training the model, at the 24th epoch, the training accuracy is 99.8% and the validation accuracy is 98.9%. This shows that our model did not over fit much. Accuracy can be further improved if Data Augmentation is performed.
We plot the confusion matrix and found out some errors made during classification task performed on the validation set. It seems that our CNN has some little troubles with the 4 digits, hey are mis-classified as 9. Sometime it is very difficult to catch the difference between 4 and 9 when curves are smooth.
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), range(cm.shape)): 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_test) # 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_test,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))
Please access the code associated with this post. It’s self Explanatory along with the Literature given here.