The flower dataset can be compiled and fit to the model using the ‘compile’ and ‘fit’ methods respectively. To the ‘fit’ method, the training dataset as well as the validation dataset are passed as parameters. The number of epochs is also defined in the ‘fit’ method.
We will be using the flowers dataset, which contains images of several thousands of flowers. It contains 5 sub-directories, and there is one sub-directory for every class.
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.
print("The model is being compiled") model.compile( optimizer='adam', loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) print("The model is being fit to the data") model.fit( train_ds, validation_data=val_ds, epochs=3 )
Code credit: https://www.tensorflow.org/tutorials/load_data/images
The model is being compiled The model is being fit to the data Epoch 1/3 92/92 [==============================] - 107s 1s/step - loss: 1.3570 - accuracy: 0.4183 - val_loss: 1.0730 - val_accuracy: 0.5913 Epoch 2/3 92/92 [==============================] - 101s 1s/step - loss: 1.0185 - accuracy: 0.5927 - val_loss: 1.0041 - val_accuracy: 0.6199 Epoch 3/3 92/92 [==============================] - 95s 1s/step - loss: 0.8691 - accuracy: 0.6529 - val_loss: 0.9985 - val_accuracy: 0.6281 <tensorflow.python.keras.callbacks.History at 0x7f2cdcbbba90>