aakashns/keras-mnist-jovian

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Handwritten Digit Recognition

Objective: Classify handwritten digits from the MNIST dataset by training a convolutional neural network (CNN) using the Keras deep learning library.

Data Preparation

We begin by downloading the data and creating training & validation sets. Keras has inbuilt helper functions to do this.

In [1]:

from keras.datasets import mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

Using TensorFlow backend.

Each sample is a 28px x 28 px image, flattened out a vector of length 784 i.e. 28x28.

In [2]:

train_images[0].shape, train_labels[0]

Out[2]:

((28, 28), 5)

In [17]:

import jovian

jovian.log_dataset({
    'from': '2019-01-01',
    'to': '2019-02-01',
    'features': ['created_at']
})

[jovian] Dataset logged.

Let's take a look at some sample images from the training set, by plotting them in a grid.

In [3]:

%matplotlib inline
import matplotlib.pyplot as plt

grid_size = 6
f, axarr = plt.subplots(grid_size, grid_size)
for i in range(grid_size):
    for j in range(grid_size):
        ax = axarr[i, j]
        ax.get_xaxis().set_visible(False)
        ax.get_yaxis().set_visible(False)
        ax.imshow(train_images[i * grid_size + j], cmap='gray')

We're going to apply the following preprocessing steps:

Reshape the image vectors into 28x28 matrices
Separate 20% of the training set to create a vaidation set
Conver the numeric labels into one-hot vectors

In [4]:

train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255

from keras.utils import to_categorical

partial_train_images = train_images[:45000]
partial_train_labels = train_labels[:45000]

validation_images = train_images[45000:]
validation_labels = train_labels[45000:]

partial_train_labels = to_categorical(partial_train_labels)
validation_labels = to_categorical(validation_labels)
test_labels = to_categorical(test_labels)

Model & Training

Now we're ready to define a simple CNN model.

In [5]:

input_shape = (28,28,1)
num_classes = 10

In [6]:

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(num_classes, activation='softmax'))

model.summary()

WARNING:tensorflow:From /usr/local/anaconda3/envs/keras-mnist-jovian/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 5408)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                54090     
=================================================================
Total params: 54,410
Trainable params: 54,410
Non-trainable params: 0
_________________________________________________________________

In [20]:

from jovian.callbacks.keras import JovianKerasCallback

model.compile(
    optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'], 
              )

In [ ]:

log_hyperparams

In [22]:

history = model.fit(
    partial_train_images, 
 
    partial_train_labels, 
    epochs=1, 
    batch_size=128, 
       callbacks=[JovianKerasCallback()],
    validation_data=(validation_images, validation_labels))

Train on 45000 samples, validate on 15000 samples
Epoch 1/1
45000/45000 [==============================] - 26s 569us/step - loss: 0.0748 - acc: 0.9783 - val_loss: 0.0814 - val_acc: 0.9759

In [9]:

import matplotlib.pyplot as plt

acc = history.history['acc']
loss = history.history['loss']
val_acc = history.history['val_acc']
val_loss = history.history['val_loss']

epochs = range(1, len(acc) + 1)

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.margins(0.05)

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.xlabel('Epochs')
plt.ylabel('Loss')
plt.margins(0.05)

plt.show()

Model Evaluation

In [10]:

test_loss, test_acc = model.evaluate(test_images, test_labels)

10000/10000 [==============================] - 1s 93us/step

In [11]:

print('Test loss:', test_loss)
print('Test acc:', test_acc)

Test loss: 0.07964395569600165
Test acc: 0.9751

We can also save the trained model's weights to disk, so we won't need to train it again.

In [12]:

model.save('mnist-cnn.h5')

Save & Commit

In [14]:

!pip install jovian --upgrade

Requirement already up-to-date: jovian in /usr/local/anaconda3/envs/keras-mnist-jovian/lib/python3.7/site-packages (0.1.89)
Requirement already satisfied, skipping upgrade: requests in /usr/local/anaconda3/envs/keras-mnist-jovian/lib/python3.7/site-packages (from jovian) (2.22.0)
Requirement already satisfied, skipping upgrade: uuid in /usr/local/anaconda3/envs/keras-mnist-jovian/lib/python3.7/site-packages (from jovian) (1.30)
Requirement already satisfied, skipping upgrade: pyyaml in /usr/local/anaconda3/envs/keras-mnist-jovian/lib/python3.7/site-packages (from jovian) (5.1)
Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in /usr/local/anaconda3/envs/keras-mnist-jovian/lib/python3.7/site-packages (from requests->jovian) (2.8)
Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /usr/local/anaconda3/envs/keras-mnist-jovian/lib/python3.7/site-packages (from requests->jovian) (3.0.4)
Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/anaconda3/envs/keras-mnist-jovian/lib/python3.7/site-packages (from requests->jovian) (2019.3.9)
Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/anaconda3/envs/keras-mnist-jovian/lib/python3.7/site-packages (from requests->jovian) (1.25.3)

In [15]:

import jovian

In [ ]:

jovian.commit()

[jovian] Saving notebook..

In [ ]: