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The augmented model can be compiled using the ‘compile’ method, which also takes the validation data and the number of epochs (number of training steps) into the method as parameters.

**Read More:**
What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.

A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model.

An image classifier is created using a keras.Sequential model, and data is loaded using preprocessing.image_dataset_from_directory. Data is efficiently loaded off disk. Overfitting is identified and techniques are applied to mitigate it. These techniques include data augmentation, and dropout. There are images of 3700 flowers. This dataset contaisn 5 sub directories, and there is one sub directory per class. They are:

daisy, dandelion, roses, sunflowers, and tulips.

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.

When the number of training examples is small, the model learns from noises or unwanted details from training examples. This negatively impacts the performance of the model on new examples.

Due to overfitting, the model will not be able to generalize well on the new dataset. There are many ways in which overfitting can be avoided. We can use drop out technique to overcome overfitting. Overfitting can be reduced by introducing dropout in the network. This is considered as a form of regularization. This helps expose the model to more aspects of the data, thereby helping the model generalize better. Following is an example:

print("Train the model to fit the given data") epochs = 15 history = model.fit( train_ds, validation_data=val_ds, epochs=epochs )

Code credit −https://www.tensorflow.org/tutorials/images/classification

Train the model to fit the given data Epoch 1/15 92/92 [==============================] - 104s 1s/step - loss: 1.5371 - accuracy: 0.3103 - val_loss: 1.1487 - val_accuracy: 0.5313 Epoch 2/15 92/92 [==============================] - 102s 1s/step - loss: 1.1414 - accuracy: 0.5087 - val_loss: 1.3204 - val_accuracy: 0.5286 Epoch 3/15 92/92 [==============================] - 102s 1s/step - loss: 1.0260 - accuracy: 0.5991 - val_loss: 0.9823 - val_accuracy: 0.6281 Epoch 4/15 92/92 [==============================] - 103s 1s/step - loss: 0.9431 - accuracy: 0.6466 - val_loss: 0.9937 - val_accuracy: 0.6417 Epoch 5/15 92/92 [==============================] - 104s 1s/step - loss: 0.8294 - accuracy: 0.6706 - val_loss: 0.8365 - val_accuracy: 0.6866 Epoch 6/15 92/92 [==============================] - 103s 1s/step - loss: 0.7584 - accuracy: 0.7101 - val_loss: 0.8471 - val_accuracy: 0.6717 Epoch 7/15 92/92 [==============================] - 102s 1s/step - loss: 0.7323 - accuracy: 0.7225 - val_loss: 0.7949 - val_accuracy: 0.6853 Epoch 8/15 92/92 [==============================] - 104s 1s/step - loss: 0.7197 - accuracy: 0.7168 - val_loss: 0.8422 - val_accuracy: 0.6839 Epoch 9/15 92/92 [==============================] - 103s 1s/step - loss: 0.7209 - accuracy: 0.7229 - val_loss: 0.7502 - val_accuracy: 0.7193 Epoch 10/15 92/92 [==============================] - 104s 1s/step - loss: 0.6334 - accuracy: 0.7590 - val_loss: 0.8248 - val_accuracy: 0.7016 Epoch 11/15 92/92 [==============================] - 104s 1s/step - loss: 0.6052 - accuracy: 0.7737 - val_loss: 0.7400 - val_accuracy: 0.7153 Epoch 12/15 92/92 [==============================] - 104s 1s/step - loss: 0.5713 - accuracy: 0.7831 - val_loss: 0.7424 - val_accuracy: 0.7098 Epoch 13/15 92/92 [==============================] - 105s 1s/step - loss: 0.5627 - accuracy: 0.7875 - val_loss: 0.7028 - val_accuracy: 0.7275 Epoch 14/15 92/92 [==============================] - 103s 1s/step - loss: 0.5254 - accuracy: 0.8029 - val_loss: 0.6900 - val_accuracy: 0.7371 Epoch 15/15 92/92 [==============================] - 103s 1s/step - loss: 0.5187 - accuracy: 0.7984 - val_loss: 0.7839 - val_accuracy: 0.7153

- The model is trained to fit the training data.
- The training goes on for 15 steps/iterations/epochs.
- This is done with the help of the ‘fit’ method.

- Related Questions & Answers
- How can Tensorflow be used to fit the data to the model using Python?
- How can Tensorflow be used to train and compile the augmented model?
- How can Tensorflow be used to visualize the augmented data from the dataset?
- How can Tensorflow be used to compile and fit the model using Python?
- How can Tensorflow be used to predict values on the new data for the augmented data?
- How can Tensorflow be used with the flower dataset to compile and fit the model?
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- How can Tensorflow be used to visualize the results of the model?
- How can Tensorflow be used to compile the model using Python?
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- How can Tensorflow be used to export the model so that it can be used later?
- How can Tensorflow and pre-trained model be used to visualize the data using Python?
- How can Tensorflow and pre-trained model after recompiling be used to visualize the data?
- How can Tensorflow be used to export the model built using Python?

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