How to train a model in TensorFlow 2.0
In this article, I am going to show how to use TensorFlow 2.0. Fortunately, the new version of TensorFlow makes it straightforward.
First, we need to import TensorFlow and Keras.
1 2 import tensorflow as tf from tensorflow import keras
As an example, I am going to use the Fashion-MNIST dataset provided by Zalando. It is one of the “built-in” datasets available in Keras, so we can load it from the datasets package.
1 2 fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
The dataset consists of gray-scale images of clothes scaled to 28x28 pixels. The values of colors are in range 0-255, so I am going to divide them by 255 to get values between 0 and 1.
1 2 train_images = train_images / 255.0 test_images = test_images / 255.0
My model starts with a convolutional layer, so I have to reshape the input. It must fit the expected (number of samples in a batch, dim1, dim2, number of channels) shape.
In my training dataset, I have 60000 images. Every one of them has 28x28 pixels and one color channel. Hence the proper shape of the input for the convolutional layer is (60000, 28, 28, 1).
To avoid hardcoding the number of images, I use the len function. I also have to repeat that operation for the test dataset.
1 2 train_images = train_images.reshape(len(train_images), 28, 28, 1) test_images = test_images.reshape(len(test_images), 28, 28, 1)
Now, I can define the model layers. I want to start with convolutional layers to give the model a chance of detecting shapes in the images. The kernel size is the size of the convolution window (if only one value is specified, it uses the same value for both dimensions).
Let’s use the following values as the layer configuration. Because it is just an example, I am not going to do any hyperparameter search. Instead of that, I picked the values manually by changing them randomly a few times and choosing the one that gives the best accuracy.
1 2 3 4 5 6 7 8 9 10 model = keras.Sequential([ keras.layers.Conv2D(64, kernel_size=3, activation='relu', input_shape=(28,28,1)), #note that we don't need the number of samples in the shape definition keras.layers.Conv2D(32, kernel_size=3, activation='relu'), keras.layers.MaxPooling2D(pool_size=(2, 2)), keras.layers.Dropout(0.1), keras.layers.Flatten(), keras.layers.Dense(32, activation='relu'), keras.layers.Dropout(0.1), keras.layers.Dense(10, activation='softmax') ])
After defining the layers, I have to configure the optimizer. Because we are trying to do multiclass classification, I define the loss as sparse categorical cross-entropy. Obviously, I am not interested in that value at all, so I am also going to track the accuracy, which is more human-friendly metric.
1 2 3 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
I will train the model for 10 epochs.
1 model.fit(train_images, train_labels, epochs=10)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Train on 60000 samples Epoch 1/10 60000/60000 [==============================] - 145s 2ms/sample - loss: 0.4928 - accuracy: 0.8244 Epoch 2/10 60000/60000 [==============================] - 143s 2ms/sample - loss: 0.3130 - accuracy: 0.8882 Epoch 3/10 60000/60000 [==============================] - 143s 2ms/sample - loss: 0.2639 - accuracy: 0.9039 Epoch 4/10 60000/60000 [==============================] - 143s 2ms/sample - loss: 0.2310 - accuracy: 0.9151 Epoch 5/10 60000/60000 [==============================] - 142s 2ms/sample - loss: 0.2107 - accuracy: 0.9215 Epoch 6/10 60000/60000 [==============================] - 142s 2ms/sample - loss: 0.1910 - accuracy: 0.9286 Epoch 7/10 60000/60000 [==============================] - 141s 2ms/sample - loss: 0.1791 - accuracy: 0.9333 Epoch 8/10 60000/60000 [==============================] - 141s 2ms/sample - loss: 0.1614 - accuracy: 0.9394 Epoch 9/10 60000/60000 [==============================] - 142s 2ms/sample - loss: 0.1533 - accuracy: 0.9413 Epoch 10/10 60000/60000 [==============================] - 141s 2ms/sample - loss: 0.1438 - accuracy: 0.9444
In the end, I can run evaluate function, and check the model’s performance on the test dataset.
1 model.evaluate(test_images, test_labels)
Output (the first number is the loss, the second one is the accuracy):
1 2 10000/10000 [==============================] - 6s 571us/sample - loss: 0.2699 - accuracy: 0.9115 [0.2698916170120239, 0.9115]
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