In the last post, I showcased an app based on a neural network, trained on the MNIST dataset using the CrysX-NN library. The app allowed the user to enter their own handwritten digits via mouse/touch screen. Since, the model only employed neural networks without any kind of convolutional layer, the performance was sub-optimal, as one would expect. For instance, the recognition of 1 and 7 was extremely problematic. This was surprising, because the accuracy on the training set was more 98%. This goes to show that the performance on the testing set may not translate that well to the real-world.

However, the lack of a convolutional layer was not the only reason. As I had shown in one of my previous posts, handwritten digit recognition is not perfect, even when using a convolutional neural network, if the model was only trained on the MNIST training set. In that post, I had made the model more robust by adding my own variations of handwritten digits to the MNIST training set. I called this extended MNIST dataset as MNIST_Plus.

Therefore, I also tried to train a simple neural network without any convolutional layer on the MNIST_Plus dataset, and see the real-world performance.

Similar to the last time, the neural network has an input layer of size 784 (28×28 pixels = 784 pixels), 1 hidden layer of size 256 with the ReLU activation function, and finally an output layer of size 10 (for 0-9 digits) with the Softmax activation function to get the probabilities for each digit.

This model didn’t show any significant improvement on the testing MNIST dataset but the real-world performance was now much better.

Below is the streamlit app using the model described above trained on MNIST_Plus. You can write a digit using the mouse/touch input. The details of the model and the app are given below the demo.

### Streamlit App

https://manassharma07-mnist-plus-mnist-plus-nn-crysx-app-fmvzzc.streamlitapp.com/

The app above is quite neat and also shows what the prediction process looks like.

The user’s handwritten input is captured in a 200×200 pixel box, which is then converted to an image. Subsequently, image processing is done to find a rectangle that completely encompasses the digit blob. Then this rectangular crop of the user input is further processed. Firstly it is converted to grayscale, then resized to a 22×22 image using BILINEAR interpolation. Then a padding of 3 pixels is applied on all the sides of the image to get a 28×28 image. This image still has pixel values in the range of 0-255. These are then normalized by dividing by 255. Then the pixel values are standardized by using the mean and standard deviation of the training MNIST_Plus dataset. Finally, the processed image is passed to the CNN model to make a prediction.

While it still not perfect and slightly inferior to the CNN model, there is a big improvement over the previous post. For example, the prediction of the digit 1 is not improved compared to before, however, it gets recognized as 2 if it has the short bottom line. The prediction is most accurate, if 1 is just drawn as a vertical line or with the top serif. Other than that it performs reasonably well with a few misclassification here and there.

There are two reasons for the bad performance of the MNIST trained model in real-world.

1. We don’t exactly know a lot of pre=processing steps used by those who collected it.

2. Some digits like 6, don’t have a lot of variations in the dataset.

### Source code for the app

https://github.com/manassharma07/MNIST-PLUS/blob/main/mnist_plus_NN_crysx_app.py

from streamlit_drawable_canvas import st_canvas import streamlit as st import matplotlib.pyplot as plt import numpy as np from PIL import Image import cv2 from crysx_nn import network @st.cache def create_and_load_model(): nInputs = 784 # No. of nodes in the input layer neurons_per_layer = [256, 10] # Neurons per layer (excluding the input layer) activation_func_names = ['ReLU', 'Softmax'] nLayers = len(neurons_per_layer) batchSize = 32 # No. of input samples to process at a time for optimization # Create the crysx_nn neural network model model = network.nn_model(nInputs=nInputs, neurons_per_layer=neurons_per_layer, activation_func_names=activation_func_names, batch_size=batchSize, device='CPU', init_method='Xavier') # Load the preoptimized weights and biases model.load_model_weights('NN_crysx_mnist_plus_98.11_streamlit_weights') model.load_model_biases('NN_crysx_mnist_plus_98.11_streamlit_biases') return model model = create_and_load_model() st.write('# MNIST_Plus Digit Recognition') st.write('## Using a `CrysX-NN` neural network model') st.write('### Draw a digit in 0-9 in the box below') # Specify canvas parameters in application stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 9) realtime_update = st.sidebar.checkbox("Update in realtime", True) # st.sidebar.markdown("## [CrysX-NN](https://github.com/manassharma07/crysx_nn)") st.sidebar.write('\n\n ## Neural Network Library Used') # st.sidebar.image('logo_crysx_nn.png') st.sidebar.caption('https://github.com/manassharma07/crysx_nn') st.sidebar.write('## Neural Network Architecture Used') st.sidebar.write('1. **Inputs**: Flattened 28x28=784') st.sidebar.write('2. **Hidden layer** of size **256** with **ReLU** activation Function') st.sidebar.write('3. **Output layer** of size **10** with **Softmax** activation Function') st.sidebar.write('Training was done for 10 epochs with Binary Cross Entropy Loss function.') # st.sidebar.image('neural_network_visualization.png') # Create a canvas component canvas_result = st_canvas( fill_color="rgba(255, 165, 0, 0.3)", # Fixed fill color with some opacity stroke_width=stroke_width, stroke_color='#FFFFFF', background_color='#000000', #background_image=Image.open(bg_image) if bg_image else None, update_streamlit=realtime_update, height=200, width=200, drawing_mode='freedraw', key="canvas", ) # Do something interesting with the image data and paths if canvas_result.image_data is not None: # st.write('### Image being used as input') # st.image(canvas_result.image_data) # st.write(type(canvas_result.image_data)) # st.write(canvas_result.image_data.shape) # st.write(canvas_result.image_data) # im = Image.fromarray(canvas_result.image_data.astype('uint8'), mode="RGBA") # im.save("user_input.png", "PNG") # Get the numpy array (4-channel RGBA 100,100,4) input_numpy_array = np.array(canvas_result.image_data) # Get the RGBA PIL image input_image = Image.fromarray(input_numpy_array.astype('uint8'), 'RGBA') input_image.save('user_input.png') # Convert it to grayscale input_image_gs = input_image.convert('L') input_image_gs_np = np.asarray(input_image_gs.getdata()).reshape(200,200) all_zeros = not np.any(input_image_gs_np) if not all_zeros: # st.write('### Image as a grayscale Numpy array') # st.write(input_image_gs_np) # Create a temporary image for opencv to read it input_image_gs.save('temp_for_cv2.jpg') image = cv2.imread('temp_for_cv2.jpg', 0) # Start creating a bounding box height, width = image.shape x,y,w,h = cv2.boundingRect(image) # Create new blank image and shift ROI to new coordinates ROI = image[y:y+h, x:x+w] mask = np.zeros([ROI.shape[0]+10,ROI.shape[1]+10]) width, height = mask.shape # print(ROI.shape) # print(mask.shape) x = width//2 - ROI.shape[0]//2 y = height//2 - ROI.shape[1]//2 # print(x,y) mask[y:y+h, x:x+w] = ROI # print(mask) # Check if centering/masking was successful # plt.imshow(mask, cmap='viridis') output_image = Image.fromarray(mask) # mask has values in [0-255] as expected # Now we need to resize, but it causes problems with default arguments as it changes the range of pixel values to be negative or positive # compressed_output_image = output_image.resize((22,22)) # Therefore, we use the following: compressed_output_image = output_image.resize((22,22), Image.BILINEAR) # PIL.Image.NEAREST or PIL.Image.BILINEAR also performs good tensor_image = np.array(compressed_output_image.getdata())/255. tensor_image = tensor_image.reshape(22,22) # Padding tensor_image = np.pad(tensor_image, (3,3), "constant", constant_values=(0,0)) # Normalization should be done after padding i guess tensor_image = (tensor_image - 0.1307) / 0.3081 # st.write(tensor_image.shape) # Shape of tensor image is (1,28,28) # st.write('### Processing steps:') # st.write('1. Find the bounding box of the digit blob and use that.') # st.write('2. Convert it to size 22x22.') # st.write('3. Pad the image with 3 pixels on all the sides to get a 28x28 image.') # st.write('4. Normalize the image to have pixel values between 0 and 1.') # st.write('5. Standardize the image using the mean and standard deviation of the MNIST_plus dataset.') # The following gives noisy image because the values are from -1 to 1, which is not a proper image format # im = Image.fromarray(tensor_image.reshape(28,28), mode='L') # im.save("processed_tensor.png", "PNG") # So we use matplotlib to save it instead plt.imsave('processed_tensor.png',tensor_image.reshape(28,28), cmap='gray') # st.write('### Processed image') # st.image('processed_tensor.png') # st.write(tensor_image.detach().cpu().numpy().reshape(28,28)) ### Compute the predictions output_probabilities = model.predict(tensor_image.reshape(1,784).astype(np.float32)) prediction = np.argmax(output_probabilities) top_3_probabilities = output_probabilities[0].argsort()[-3:][::-1] ind = output_probabilities[0].argsort()[-3:][::-1] top_3_certainties = output_probabilities[0,ind]*100 st.write('### Prediction') st.write('### '+str(prediction)) st.write('MNIST_Plus Dataset (with more handwritten samples added by me) available as PNGs at: https://github.com/manassharma07/MNIST-PLUS/tree/main/mnist_plus_png') st.write('## Breakdown of the prediction process:') st.write('### Image being used as input') st.image(canvas_result.image_data) st.write('### Image as a grayscale Numpy array') st.write(input_image_gs_np) st.write('### Processing steps:') st.write('1. Find the bounding box of the digit blob and use that.') st.write('2. Convert it to size 22x22.') st.write('3. Pad the image with 3 pixels on all the sides to get a 28x28 image.') st.write('4. Normalize the image to have pixel values between 0 and 1.') st.write('5. Standardize the image using the mean and standard deviation of the MNIST_Plus training dataset.') st.write('### Processed image') st.image('processed_tensor.png') st.write('### Prediction') st.write(str(prediction)) st.write('### Certainty') st.write(str(output_probabilities[0,prediction]*100) +'%') st.write('### Top 3 candidates') # st.write(top_3_probabilities) st.write(str(top_3_probabilities)) st.write('### Certainties %') # st.write(top_3_certainties) st.write(str(top_3_certainties)) st.write('### Code used for training the neural network: [Jupyter Notebook](https://github.com/manassharma07/crysx_nn/blob/main/examples/NN_MNIST_plus_from_raw_png_crysx.ipynb)')

### Pretrained Neural Network weights and biases for the CrysX-NN model

**Biases:** https://github.com/manassharma07/MNIST-PLUS/blob/main/NN_crysx_mnist_plus_98.11_streamlit_biases.npz

You can download it and load it in your python code using:

model.load_model_weights('NN_crysx_mnist_plus_98.11_weights') model.load_model_biases('NN_crysx_mnist_plus_98.11_biases')

### Code used for training the model

https://github.com/manassharma07/crysx_nn/blob/main/examples/NN_MNIST_plus_from_raw_png_crysx.ipynb

### Details of the Neural Network

**Optimizer**: Stochastic Gradient Descent

**Learning Rate** = 0.3

**Number of epochs** = 10

**Batch size** = 200

**Loss function**: Categorical Cross Entropy loss

#### Code snippet for creation and training of Neural network

from crysx_nn import mnist_utils as mu import numpy as np # Download MNIST_orig and MNIST_plus datasets (May take upto 5 min) mu.downloadMNIST() ## Load the training dataset from MNIST_orig in memory (May take upto 5 min) path = 'MNIST-PLUS-PNG/mnist_plus_png' trainData, trainLabels = mu.loadMNIST(path_main=path, train=True, shuffle=True) ## Normalize within the range [0,1.0] trainData = trainData/255 # Normalize trainData_mean = trainData.mean() trainData_std = trainData.std() ## Standardize the data so that it has mean 0 and variance 1 trainData = (trainData - np.mean(trainData)) / np.std(trainData) ## Convert labels to one-hot vectors trainLabels = mu.one_hot_encode(trainLabels, 10) ## Flatten the input numpy arrays (nSamples,28,28)->(nSamples, 784) trainData = trainData.reshape(trainData.shape[0], 784) ## Let us create a NN using CrysX-NN now nInputs = 784 # No. of nodes in the input layer neurons_per_layer = [256, 10] # Neurons per layer (excluding the input layer) activation_func_names = ['ReLU', 'Softmax'] nLayers = len(neurons_per_layer) nEpochs = 10 batchSize = 200 # No. of input samples to process at a time for optimization from crysx_nn import network model = network.nn_model(nInputs=nInputs, neurons_per_layer=neurons_per_layer, activation_func_names=activation_func_names, batch_size=batchSize, device='CPU', init_method='Xavier') model.lr = 0.3 ## Check the model details model.details() model.visualize() ## Optimize/Train the network inputs = trainData.astype(np.float32) outputs = trainLabels.astype(np.float32) # Run optimization # model.optimize(inputs, outputs, lr=0.3,nEpochs=nEpochs,loss_func_name='CCE', miniterEpoch=1, batchProgressBar=True, miniterBatch=100) # To get accuracies at each epoch model.optimize(inputs, outputs, lr=0.3,nEpochs=nEpochs,loss_func_name='CCE', miniterEpoch=1, batchProgressBar=True, miniterBatch=100, get_accuracy=True) ## Error at each epoch print(model.errors) ## Accuracy at each epoch print(model.accuracy) ## Save model weights and biases # Save weights model.save_model_weights('NN_crysx_mnist_plus_98.11_weights') # Save biases model.save_model_biases('NN_crysx_mnist_plus_98.11_biases') ## Load model weights and biases from files model.load_model_weights('NN_crysx_mnist_plus_98.11_weights') model.load_model_biases('NN_crysx_mnist_plus_98.11_biases') ## Test data set path = 'MNIST-PLUS-PNG/mnist_plus_png' testData, testLabels = mu.loadMNIST(path_main=path, train=False, shuffle=True) ## Normalize within the range [0,1.0] testData = testData/255. # Normalize ## Standardize the data so that it has mean 0 and variance 1 # Use the mean and std of training data ********** testData = (testData - trainData_mean) / trainData_std ## Convert labels to one-hot vectors testLabels = mu.one_hot_encode(testLabels, 10) ## Flatten the input numpy arrays (nSamples,28,28)->(nSamples, 784) testData = testData.reshape(testData.shape[0], 784) ## Performance on Test data # Convert to float32 arrays inputs = testData.astype(np.float32) outputs = testLabels.astype(np.float32) predictions, error, accuracy = model.predict(inputs, outputs, loss_func_name='CCE', get_accuracy=True) print('Error:',error) print('Accuracy %:',accuracy*100)