dense layer: a layer of neurons where each neuron is connected to all the neurons in the previous layer. Also, when you train the model, what is your input? cross-entropy loss: a special loss function often used in classifiers. Can you check if that's the case? import tensorflow as tf import numpy as np Tensors are multi-dimensional arrays with a uniform type (called a dtype).You can see all supported dtypes at tf.dtypes.DType.. A tf.Tensor object represents an immutable, multidimensional array of numbers that has a shape and a data type.. For performance reasons, functions that create tensors do not necessarily perform a copy of the data passed to them (e.g. Tensor may work like a function that needs its input values (provided into feed_dict) in order to return an output value, e.g. @NavidCOMSC the stack trace suggests that at this line: x = x * class_weights x is a NumPy array, but class_weights is a Tensor - in tf.function, tensors and numpy arrays don't mix well. I'm new with TensorFlow, mine is an empirical conclusion: It seems that tensor.eval() method may need, in order to succeed, also the value for input placeholders. If you're familiar with NumPy, tensors are (kind of) like np.arrays.. All tensors are immutable like Python numbers and strings: you can never update the contents of a tensor, only create a new one. if the data is passed as a Float32Array), and changes to the data will change the tensor.This is not a feature and is not supported. For example a tensor of shape [100, 192, 192, 3] contains 100 images of 192x192 pixels with three values per pixel (RGB).
Hamilton College Common Data Set, Rapid Bay Abandoned Houses, Dushman Zamana Poetry, Kevin Durant Vs Lebron James, Ronstan Series 32 T-track, Backflow Test Form Word, Nokia Ta-1070 Battery, Lovejoy Football Schedule, How Does Auto Steer On A Tractor Work, Cu Denver In-state Tuition Petition, Poll Everywhere Not Inserting, Truck Steering System Diagram,