Deep Learning with Tensorflow 2.0 Tutorial – Getting Started with Tensorflow 2.0 and Keras for Beginners
Classification using Fashion MNIST dataset
What is TensorFlow?
TensorFlow is one of the best libraries to implement deep learning.
TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by
TensorFlow architecture works in three parts:
- Preprocessing the data
- Build the model
- Train and estimate the model
Why Every Data Scientist should Learn Tensorflow 2.x and not Tensorflow 1.x?
- API Cleanup
- Eager execution
- No more globals
- Functions, not sessions (session.run())
- Use Keras layers and models to manage variables
- It is faster
- It takes less space
- More consistent
For more information you can visit these links:-
- Google I/O: https://www.youtube.com/watch?v=lEljKc9ZtU8
install TensorFlow by running the following command in the
Anaconda admin shell.
pip install tensorflow
If you have
GPU in your machine then you can run this command to install the GPU version of
pip install tensorflow-gpu
Fashion MNIST dataset
We are going to use the Fasion Mnist dataset. It consists of a training set of 60,000 examples and a test set of 10,000 examples. All the 70,000 images show individual articles of clothing at low resolution (28 by 28 pixels). The grayscale images are from 10 categories(T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot) as seen here:
|Figure 1. Fashion-MNIST samples (by Zalando, MIT License).|
You can download the actual dataset from here.
In this notebook we are goint to use the
fashion_mnist dataset which is available in
import tensorflow as tf from tensorflow import keras
The necessary python libraries are imported here-
numpyis used to perform basic operations.
matplotlibis used to visualize the results.
pandasis used to read the dataset.
import numpy as np import pandas as pd import matplotlib.pyplot as plt
Here we are loading the
fashion_mnist dataset from
mnist = keras.datasets.fashion_mnist
The dataset is downloaded in
Now we load the data into real variables using
load_data(). It return 2 tuples. The first tupes has the
training data and the second tuple has the
(X_train, y_train), (X_test, y_test) = mnist.load_data()
shape we can see that it has 60,000 images for training and each image is of size 28×28 in
X_train and a corresponding label for each image in
((60000, 28, 28), (60000,))
np.max() gives the maximum value. Hence the maximum value in
X_train is 255. We can even see the minimum value using
np.min(X_train). The minimum value will be 0.
np.mean() gives the mean value. The mean value in
X_train is 72.94
As we know the images are divided into 10 categories. The 10 categories are encoded using a numerical value as show below.
array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)
These are all the class name in their proper order.
top is encoded as
trouser is encoded as
1 and so on.
class_names = ['top', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']
(60000, 28, 28)
The testing data set containes 10000 images of size 28×28.
(10000, 28, 28)
Here we have plotted the second image of our training set i.e. the image at index 1.
plt.figure() function in pyplot module of matplotlib library is used to create a new figure. The
plt.imshow() function in pyplot module of matplotlib library is used to display data as an image.
plt.colorbar() displays the colour bar besides the image. You can see that the values are between
0 and 255.
plt.figure() plt.imshow(X_train) plt.colorbar()
As you can see the image is a top and the value at index 1 of
y_train also corresponds to the class name
array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)
Neural Network model doesn’t take value greater than 1. So we need to bring all the values between
0 and 1. To do this we will divide all the values in the training and testing dataset by 255 as the greatest value in our dataset is 255.
X_train = X_train/255.0 X_test = X_test/255.0
Now we can see that all the values are between
0 and 1.
plt.figure() plt.imshow(X_train) plt.colorbar()
Build the model with TF 2.0
We will import the necessary layers to build the model. The Neural Network is constructed from 3 type of layers:
Input layer— initial data for the neural network.
Hidden layers— intermediate layer between input and output layer and place where all the computation is done.
Output layer— produce the result for given inputs.
from tensorflow.keras import Sequential from tensorflow.keras.layers import Flatten, Dense
Flatten() is used as the
input layer to convert the data into a 1-dimensional array for inputting it to the next layer. Our image 2D image will be converted to a single 1D column.
input_shape = (28,28) because the size of our input image is 28×28.
Dense() layer is the regular deeply connected neural network layer. It is most common and frequently used layer. We have a dense layer with
128 neurons with activation function
relu. The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero.
output layer is a
dense layer with
10 neurons because we have 10 classes. The activation function used is
softmax. Softmax converts a real vector to a vector of categorical probabilities. The elements of the output vector are in range (0, 1) and sum to 1. Softmax is often used as the activation for the last layer of a classification network because the result could be interpreted as a probability distribution.
model = Sequential() model.add(Flatten(input_shape = (28, 28))) model.add(Dense(128, activation = 'relu')) model.add(Dense(10, activation = 'softmax'))
Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= flatten_1 (Flatten) (None, 784) 0 _________________________________________________________________ dense_2 (Dense) (None, 128) 100480 _________________________________________________________________ dense_3 (Dense) (None, 10) 1290 ================================================================= Total params: 101,770 Trainable params: 101,770 Non-trainable params: 0 _________________________________________________________________
Loss Function: A loss function is used to optimize the parameter values in a neural network model. Loss functions map a set of parameter values for the network onto a scalar value that indicates how well those parameter accomplish the task the network is intended to do.
Optimizer: Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses.
Metrics: A metric is a function that is used to judge the performance of your model. Metric functions are similar to loss functions, except that the results from evaluating a metric are not used when training the model.
Here we are
compiling the model and
fitting it to the training data. We will use 10
epochs to train the model. An epoch is an iteration over the entire data provided.
model.compile(optimizer='adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy']) model.fit(X_train, y_train, epochs = 10)
Train on 60000 samples Epoch 9/10 60000/60000 [==============================] - 5s 79us/sample - loss: 0.2472 - accuracy: 0.9081 Epoch 10/10 60000/60000 [==============================] - 5s 80us/sample - loss: 0.2374 - accuracy: 0.9120
Now we will evaluate the accuracy using the
test data. We have got an accuracy of 88.41%.
test_loss, test_acc = model.evaluate(X_test, y_test) print(test_acc)
10000/10000 [==============================] - 1s 81us/sample - loss: 0.3312 - accuracy: 0.8841 0.8841
Now we are going to do predictions using
sklearn and see the accuracy. For that we will import
from sklearn.metrics import accuracy_score
predict_classes generates class predictions for the input samples. Here we are giving
X_test containing 10000 images as the input. We are even predicting the accuracy. In multilabel classification,
accuracy_score computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in
y_pred = model.predict_classes(X_test) accuracy_score(y_test, y_pred)
Now we will use this model to perform predictions on an input image.
array([9, 2, 1, ..., 8, 1, 5], dtype=int64)
predict() returns an array unlike
predict_class() which returns categorical values. The array contains the confidence level for each of the class.
pred = model.predict(X_test) pred
array([[2.3493649e-07, 1.7336136e-09, 3.9774801e-09, ..., 1.7785656e-03, 3.6985359e-08, 9.9811894e-01], [7.2894422e-06, 5.1075269e-15, 9.9772221e-01, ..., 2.2120526e-12, 6.0182694e-09, 2.4706193e-16], [1.3015335e-07, 9.9999988e-01, 3.9984838e-12, ..., 1.3045939e-23, 3.1408490e-11, 2.1719039e-15], ...
As you can see for the test image at index 0,
predict has retured this array. In this the value at index 9 is the greatest. This shows that the model has predicted the test image at index 0 to have class 9 which is
array([2.3493649e-07, 1.7336136e-09, 3.9774801e-09, 1.0847069e-08, 2.9475926e-09, 1.0172284e-04, 3.8994517e-07, 1.7785656e-03, 3.6985359e-08, 9.9811894e-01], dtype=float32)
argmax() returns the index of the maximum value. Hence we can see that the predicted class is 9 as maximum value is found at index position 9.
For the second image in the
test data i.e. the image at index 1 the predicted class is 2 which is
The model gives an accuracy of
91.2% on the
training data and
test data. Hence the model is
overfitting the training data. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model. The problem is that these concepts do not apply to new data and negatively impact the models ability to generalize. To avoid overfitting we can use
Convolutional Neural Networks.