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Try this: imgs = tf.convert_to_tensor(images[0]) If all of your images are of the same shape. Otherwise you have to resize them to a same dimension then call the code.

What does it mean that classes are mutually exclusive but soft-labels are accepted? As it can be seen from here, tf.nn.softmax produces just the result of applying the softmax function to an inpu …

As the response for the second question, again, if you don't specify the initial weights, TensorFlow itself will use Glorot method for initialization. …

Although your input data is three dimensional, you have to use Conv2D for your task. I guess Conv3d is used for data with temporal characteristic, yours is just a simple picture. To illustrate why you …

Based on the solution here you have different choices: The simplest solution is to revert back to Pandas 0.19.2. For this purpose use the following command in your command line or terminal. conda in …

#x = tf.reshape(x, shape=[-1, 299, 299, 3]) change the above line to the following code snippet. dim = x.get_shape().as_list() x = tf.reshape(x, shape = [-1, *dim[1:]]) The same behavior applies …

You can find good tips here and here which have discussed storing and loading models in Tensorflow. …

The point in using batch training is that you can't take a step using all data due to the size of the training data which is really big. Consequently, you may want to use batch optimisation techniques …

It seems that after some epochs your training oscillates. I guess the reason is that the learning rate is high. Try to set the decay parameter of Adam optimizer to a number more than one and resume tr …

You are initializing your weights to zero. This will cause symmetric problem in updating weights. Use Xavier or He technique for initialization and train for some epochs.

I have the following suggestion: The size of your data-set is small, you should increase the sample size. You also have to use data augmentation methods for improving the performance, translation, o …

In TensorFlow there are different convolution layers. …

Basically I guess TensorFlow does not support decision trees. … You can see from here that there are other learning algorithms implemented in TensorFlow which are not deep models. You can take a look at here for tracking algorithms implemented in TensorFlow. …

It is a bit technical and depends where you use your JavaScript code. It is used in both backend and frontend apps. My opinion is that using that in frontend apps can help your ML algorithms run on di …

Its homework is written in Keras and TensorFlow. It is a very good point to begin. … Furthermore, you can take a look at the official paper of google research team about TensorFlow where you can read TensorFlow is an interface for expressing machine learning algorithms, and an implementation …