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# MNIST-Dataset-Digit-Recognizer ¶

This classic Dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.

We will use Keras with TensorFlow as the main package to create a simple neural network to predict, as accurately as we can, digits from handwritten images. Also, we will be experimenting with various optimizers: the plain vanilla Stochastic Gradient Descent optimizer and the Adam optimizer. However, there are many other parameters, such as training epochs which will we will not be experimenting with. Lastly, we introduce dropout, a form of regularisation, in our neural networks to prevent overfitting.

In [3]:
!pip3 install tensorflow

Defaulting to user installation because normal site-packages is not writeable
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The data set contains 60,000 traning images and 10000 testing images. Here I split the data into training and testing datasets respectively. The x_train & x_test contains grayscale codes while y_test & y_train contains labels from 0–9 which represents the numbers.

In [4]:
import tensorflow as tf
#MNIST ("Modified National Institute of Standards and Technology")
mnist = tf.keras.datasets.mnist

In [5]:
import matplotlib.pyplot as plt
print(x_train[0])

[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   3  18  18  18 126 136
175  26 166 255 247 127   0   0   0   0]
[  0   0   0   0   0   0   0   0  30  36  94 154 170 253 253 253 253 253
225 172 253 242 195  64   0   0   0   0]
[  0   0   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251
93  82  82  56  39   0   0   0   0   0]
[  0   0   0   0   0   0   0  18 219 253 253 253 253 253 198 182 247 241
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0  14   1 154 253  90   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0 139 253 190   2   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0  11 190 253  70   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0  35 241 225 160 108   1
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0  81 240 253 253 119
25   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0  45 186 253 253
150  27   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252
253 187   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 249
253 249  64   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253
253 207   2   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0  39 148 229 253 253 253
250 182   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253 253 201
78   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0  23  66 213 253 253 253 253 198  81   2
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0  18 171 219 253 253 253 253 195  80   9   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0  55 172 226 253 253 253 253 244 133  11   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0 136 253 253 253 212 135 132  16   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]]

In [6]:
#SHOW BINARY COLOUR AMP THAT IS GREY SCALE IMAGE
plt.imshow(x_train[0],cmap=plt.cm.binary)

Out[6]:
<matplotlib.image.AxesImage at 0x7fc8252e7950>
In [7]:
x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis=1)


check the shape of the dataset to see if it is compatible to use in for CNN. here (60000,28,28) is our result which means that we have 60000 images in our dataset and size of each image is 28 * 28 pixel.

In [8]:
print(x_train[0].shape)

(28, 28)


## Building the Model: ¶

Use Keras API to build the model. here i import the Sequential Model from Keras and add Conv2D, MaxPooling, Flatten, Dropout, and Dense layers. Dropout layers fight with the overfitting by disregarding some of the neurons while training while Flatten layers flatten 2D arrays to 1D array before building the fully connected layers.

In [11]:
#sequential model
model = tf.keras.models.Sequential()

In [12]:
model.add(tf.keras.layers.Flatten())


## Compiling and fitting the Model: ¶

So far, we have created an non-optimized empty CNN. Then I set an optimizer with a given loss function which uses a metric and fit the model by using our train data. The ADAM optimizer is said to outperform the other optimizers, that’s why I used that.

In [15]:
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])

In [16]:
model.fit(x_train, y_train, epochs= 10)

Epoch 1/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.4850 - accuracy: 0.8645
Epoch 2/10
1875/1875 [==============================] - 5s 2ms/step - loss: 0.1146 - accuracy: 0.9667
Epoch 3/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0697 - accuracy: 0.9773
Epoch 4/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0514 - accuracy: 0.9831
Epoch 5/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0402 - accuracy: 0.9869
Epoch 6/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0290 - accuracy: 0.9906
Epoch 7/10
1875/1875 [==============================] - 5s 2ms/step - loss: 0.0223 - accuracy: 0.9929
Epoch 8/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0192 - accuracy: 0.9930
Epoch 9/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0157 - accuracy: 0.9947
Epoch 10/10
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0119 - accuracy: 0.9959

Out[16]:
<tensorflow.python.keras.callbacks.History at 0x7fc826bd4a90>
In [17]:
val_loss, val_acc = model.evaluate(x_test, y_test)

313/313 [==============================] - 1s 2ms/step - loss: 0.1124 - accuracy: 0.9728


### Model Evaluation: ¶

When this model is evaluated we see that just 10 epochs gave use the accuracy of 98.59% at a very low loss.

In [18]:
print(val_loss)
print(val_acc)

0.11244665831327438
0.9728000164031982

In [19]:
model.save('mnist_digit.model')

INFO:tensorflow:Assets written to: mnist_digit.model/assets

In [20]:
predictions = model.predict(x_test)
print(predictions)

[[3.22702544e-14 3.51989420e-14 5.57353896e-09 ... 1.00000000e+00
1.60817892e-13 2.40715542e-13]
[8.56872416e-19 1.27712987e-08 1.00000000e+00 ... 2.81515991e-11
1.06037565e-17 1.32803862e-22]
[3.79793225e-10 9.99989152e-01 5.12245833e-07 ... 1.00320940e-05
2.02710496e-07 4.03521799e-11]
...
[8.25261289e-17 3.40463356e-12 2.62771077e-17 ... 1.34488722e-08
1.85024285e-14 1.18482317e-08]
[1.26037147e-12 6.74442978e-13 1.62169692e-14 ... 3.93779592e-10
2.12413988e-06 7.47177758e-15]
[1.57967750e-10 3.43449249e-12 8.06131203e-11 ... 2.16600704e-13
3.22796434e-12 2.31840290e-13]]

In [31]:
import numpy as np

print(np.argmax(predictions[0]))

7

In [32]:
plt.imshow(x_test[0])

Out[32]:
<matplotlib.image.AxesImage at 0x7fc2ec6824d0>
In [21]:
model.evaluate(x_test, y_test)

313/313 [==============================] - 1s 2ms/step - loss: 0.1124 - accuracy: 0.9728

Out[21]:
[0.11244665831327438, 0.9728000164031982]

Here we select an image and run it through to get the prediction then display both the image and prediction to see if its accurate.

In [23]:
image_index = 285
plt.imshow(x_test[image_index].reshape(28, 28),cmap='Greys')
predict = x_test[image_index].reshape(28,28)
pred = model.predict(x_test[image_index].reshape(1, 28, 28, 1))
print(pred.argmax())

2

In [25]:
image_index = 6000
plt.imshow(x_test[image_index].reshape(28, 28),cmap='Greys')
predict = x_test[image_index].reshape(28,28)
pred = model.predict(x_test[image_index].reshape(1, 28, 28, 1))
print(pred.argmax())

9

In [ ]: