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How can Tensorflow be used to visualize the loss versus training using Python?
Tensorflow can be used to visualize the loss versus training 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("Visualizing the data - loss versus training steps") plt.figure() plt.ylabel("Loss") plt.xlabel("Training Steps") plt.ylim([0,2]) plt.plot(batch_stats_callback.batch_losses)
- After some training iterations, the model can be seen as making process.
- This progress is visualized on the console, as training steps versus loss.
- How can Tensorflow be used to visualize training results using Python?
- How can Tensorflow be used to visualize the data using Python?
- How can Tensorflow be used to visualize the flower dataset using Python?
- How can Tensorflow and pre-trained model be used to visualize the data using Python?
- How can Tensorflow be used to visualize the results of the model?
- How can Tensorflow be used to pre-process the flower training dataset?
- How can Tensorflow and pre-trained model be used to continue training the model using Python?
- How can Tensorflow be used to prepare the IMDB dataset for training in Python?
- How can Tensorflow be used with estimators to visualize the titanic data?
- How can Tensorflow be used to visualize the augmented data from the dataset?
- How can Tensorflow be used with flower dataset to continue training the model?
- How can Tensorflow be used to split the flower dataset into training and validation?
- How can Tensorflow be used to standardize the data using Python?
- How can Tensorflow be used to compile the model using Python?
- How can Tensorflow be used to train the model using Python?