Found 178 Articles for Tensorflow

A neural network that contains at least one layer is known as a convolutional layer. A convolutional neural network would generally consist of some combination of the below mentioned layers:Convolutional layersPooling layersDense layersConvolutional Neural Networks have been used to produce great results for a specific kind of problems, such as image recognition.  It is a Deep Learning algorithm that takes an image as input, assigns importance to it, i.e. the algorithm learns to assign weights and biases to values. This helps differentiate one object from the other.The amount of pre-processing required in a ConvNet is lesser than other classification algorithms. ... Read More

Tensorflow is a machine learning framework that is provided by Google. It is an open-source framework used in conjunction with Python to implement algorithms, deep learning applications and much more.TensorFlow is used in research and for production purposes and has optimization techniques that help in performing complicated mathematical operations quickly. This is because it uses NumPy and multi-dimensional arrays. These multi-dimensional arrays are also known as ‘tensors’. The framework supports working with deep neural network. It is highly scalable, and comes with many popular datasets. It uses GPU computation and automates the management of resources. It comes with multitude of ... Read More

Tensorflow can be used to create a model that tracks internal layers by creating a sequential model and using this model to call ‘tf.zeros’ method.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?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 ... Read More

Tensorflow can be used to compose layers by defining a class that inherits from ‘ResnetIdentityBlock’. This is used to define a block which can be used to compose the layers.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?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. 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 ... Read More

Tensorflow can be used to call the layer, and get the variables present in the layer, by first defining the layer, and using ‘layer.kernel’, and ‘layer.bias’ to access these variables. The ‘tf.zeros’ is used, and the layer can be iterated over and called.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?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 ... Read More

Tensorflow can be used to implement custom layers by creating a class and defining a function to build the layers, and defining another function to call the matrix multiplication by passing the input to it.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?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 ... Read More

Tensorflow can be used to get the variables in a layer by displaying the variables in the layer using ‘layer.Variables’, and then using ‘layer.kernel’, and ‘layer.bias’ to access these variables.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?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 ... Read More

Tensorflow can be used to construct an object for customized layers by first creating the required layers, and then using this layer on tf.zeros method.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?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 ... Read More

Tensorflow can be used to confirm that the saved model can be reloaded by using the ‘load_model’ and using the ‘predict’ method. The reloaded model can be used predict the data.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?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 ... Read More

Tensorflow can be used to export the model so that it can be used later by first saving the model using ‘save’ method.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?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 ... Read More