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How can MNIST data be used with TensorFlow for recognizing handwritten digits?
Tensorflow is a machine learning framework that is provided by Google. It is an open−source framework used in conjunction with Python to implement algorithms, deep learning applications and much more. It is used in research and for production purposes.
The ‘tensorflow’ package can be installed on Windows using the below line of code −
pip install tensorflow
Tensor is a data structure used in TensorFlow. It helps connect edges in a flow diagram. This flow diagram is known as the ‘Data flow graph’. Tensors are nothing but multidimensional array or a list.
We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.
The MNIST dataset contains handwritten digits, wherein 60000 of them are used for training the model and 10000 of them are used to test the trained model. These digits have been size−normalized and centered to fit a fixed−size image.
Following is the code −
Example
import tensorflow as tf
mnist = tf.keras.datasets.mnist
print("Data is being loaded")
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10)
])
predictions = model(x_train[:1]).numpy()
print("The predictions are : ")
print(predictions)
tf.nn.softmax(predictions).numpy()
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
print("The loss function has been defined")
loss_fn(y_train[:1], predictions).numpy()
model.compile(optimizer='adam',
loss=loss_fn,
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=9)
print("The data is being fit to the model")
model.evaluate(x_test, y_test, verbose=2)
print("The results are being evaluated")
probability_model = tf.keras.Sequential([model,
tf.keras.layers.Softmax() ]) print("The predictions are : ") probability_model(x_test[:8])Code credit − https://www.tensorflow.org/tutorials/quickstart/beginner
Output
Data is being loaded The predictions are : [[-0.77715474 -0.21606012 -0.04190525 -0.22804758 0.03612506 0.5986039 0.6838669 -0.40150493 0.55429333 0.55918723]] The loss function has been defined Epoch 1/9 1875/1875 [==============================] - 4s 2ms/step - loss: 0.4934 - accuracy: 0.8564 Epoch 2/9 1875/1875 [==============================] - 4s 2ms/step - loss: 0.1511 - accuracy: 0.9566 Epoch 3/9 1875/1875 [==============================] - 4s 2ms/step - loss: 0.1046 - accuracy: 0.9690 Epoch 4/9 1875/1875 [==============================] - 4s 2ms/step - loss: 0.0861 - accuracy: 0.9733 Epoch 5/9 1875/1875 [==============================] - 4s 2ms/step - loss: 0.0712 - accuracy: 0.9779 Epoch 6/9 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0621 - accuracy: 0.9798 Epoch 7/9 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0544 - accuracy: 0.9822 Epoch 8/9 1875/1875 [==============================] - 4s 2ms/step - loss: 0.0505 - accuracy: 0.9837 Epoch 9/9 1875/1875 [==============================] - 4s 2ms/step - loss: 0.0442 - accuracy: 0.9853 The data is being fit to the model 313/313 - 0s - loss: 0.0691 - accuracy: 0.9792 The results are being evaluated The predictions are : <tf.Tensor: shape=(8, 10), dtype=float32, numpy= array([[5.02094943e-09, 9.46477272e-11, 2.06162738e-07, 4.83285694e-05, 4.39524014e-13, 1.28035786e-08, 2.14148154e-16, 9.99951243e-01, 7.93324517e-09, 2.28497953e-07], [1.91046940e-12, 2.99942280e-06, 9.99997020e-01, 1.57965779e-10, 8.59648663e-21, 6.85132751e-09, 3.96876629e-11, 1.56446678e-15, 7.72609667e-12, 1.23343747e-16], [2.50448129e-09, 9.99879003e-01, 4.76238492e-05, 2.03783550e-08, 8.07857646e-07, 9.36941333e-08, 1.20849165e-06, 5.47862328e-05, 1.65028414e-05, 1.13786543e-10], [9.99895811e-01, 5.68212422e-10, 5.65115661e-05, 1.29087857e-06, 1.05537436e-06, 1.18774800e-07, 4.08086999e-05, 3.00701231e-06, 3.09539394e-09, 1.48017170e-06], [2.68701024e-06, 8.04860432e-12, 2.38641114e-05, 1.81536635e-07, 9.98934567e-01, 8.67565220e-08, 6.51489245e-07, 2.03823347e-05, 2.35504160e-07, 1.01726933e-03], [3.68854253e-11, 9.99950528e-01, 4.57934220e-07, 5.73739001e-10, 1.50253769e-07, 1.97563521e-10, 2.10510054e-09, 4.76488349e-05, 1.17310219e-06, 1.29714104e-11], [5.41477474e-10, 2.30981789e-09, 2.14095284e-08, 1.05171161e-07, 9.97043908e-01, 5.71491137e-06, 4.81293283e-10, 4.48020655e-05, 6.65718471e-05, 2.83887982e-03], [1.04743374e-10, 1.53262540e-08, 5.02297407e-05, 2.83393019e-04, 2.03316798e-04, 2.68098956e-05, 9.15681864e-10, 1.22959409e-05, 7.81168455e-06, 9.99416113e-01]], dtype=float32)>
Explanation
The required packages are downloaded and aliased.
The MNIST dataset is downloaded from the source.
The dataset is split into training and testing data.
A sequential model is built using the ‘keras’ package.
The predictions are made on test dataset.
The loss function is defined using the ‘SparseCategoricalCrossentropy’ method present in ‘Keras’ package.
The model is compiled and then fit to the data.
This trained data is evaluated by using the test dataset.
The predictions are displayed on the console.
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