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How can TensorFlow be used to visualise the trade off between accuracy and training steps?
Tensorflow can be used to visualize the trade-off between accuracy and training steps using the ‘matplotlib’ library and ‘plot’ method to plot the data.
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.
The intuition behind transfer learning for image classification is, if a model is trained on a large and general dataset, this model can be used to effectively serve as a generic model for the visual world. It would have learned the feature maps, which means the user won’t have to start from scratch by training a large model on a large dataset.
TensorFlow Hub is a repository that contains pre-trained TensorFlow models. TensorFlow can be used to fine-tune learning models.
We will understand how to use models from TensorFlow Hub with tf.keras, use an image classification model from TensorFlow Hub. Once this is done, transfer learning can be performed to fine-tune a model for customized image classes. This is done by using a pretrained classifier model to take an image and predict what it is. This can be done without needing any training.
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("Accuracy versus training steps") plt.figure() plt.ylabel("Accuracy") plt.xlabel("Training Steps") plt.ylim([0,1]) plt.plot(batch_stats_callback.batch_acc)
- The visualization of accuracy versus training steps is done.
- How can Tensorflow be used to pre-process the flower training dataset?
- How can Tensorflow be used to visualize training results using Python?
- How can Tensorflow be used to split the flower dataset into training and validation?
- How can TensorFlow be used to create a plot that visualizes training and validation accuracy in the trained IMDB dataset in Python?
- How can Tensorflow be used with flower dataset to continue training the model?
- How can Tensorflow be used to visualize the loss versus training using Python?
- How can Tensorflow be used to prepare the IMDB dataset for training in Python?
- How can Tensorflow be used with pre-trained model to build the training and validation dataset?
- How can Tensorflow and pre-trained model be used to continue training the model using Python?
- How can Tensorflow be used to load the flower dataset and model off the disk using Python?
- How can Tensorflow be used to split the Illiad dataset into training and test data in Python?
- How can Keras be used to recreate the model and check its accuracy?
- How can Tensorflow and Estimator be used to define input function for training and evaluation of dataset?
- How can Tensorflow be used with Estimators to visualise the plot of number of males v/s females?
- How can Tensorflow and pre-trained model be used to see the changes during training after every epoch?