## Keras – Dense and Conv2D values

Images that I would like to use to train the network are about the size of `4000px*3000px`

and about `40k`

of them, sorted in `250`

classes.

I have made a CNN shown below:

Images that I would like to use to train the network are about the size of `4000px*3000px`

and about `40k`

of them, sorted in `250`

classes.

I have made a CNN shown below:

I am using Keras with TensorFlow to implement a deep neural network. When I plot the loss and number of iterations, there is a significant jump in loss after each epoch. In reality, the loss of each mini-batch should vary from each other, but Keras calculates the moving average of the loss over the mini-batches, that’s why we obtain a smooth curve instead of an arbitrary one. The array of the moving average is reset after each epoch because of which we can observe a jump in the loss.

I am trying to reconcile my understand of LSTMs and pointed out here in this post by Christopher Olah implemented in Keras. I am following the blog written by Jason Brownlee for the Keras tutorial. What I am mainly confused about is,

I have trained a binary classification model with CNN, and here is my code

This is my test code:

Hi I have been trying to make a custom loss function in keras for dice_error_coefficient. It has its implementations in **tensorboard** and I tried using the same function in keras with tensorflow but it keeps returning a **NoneType** when I used **model.train_on_batch** or **model.fit** where as it gives proper values when used in metrics in the model. Can please someone help me out with what should i do? I have tried following libraries like Keras-FCN by ahundt where he has used custom loss functions but none of it seems to work. The target and output in the code are y_true and y_pred respectively as used in the losses.py file in keras.

So I’ve been following Google’s official tensorflow guide and trying to build a simple neural network using Keras. But when it comes to training the model, it does not use the entire dataset (with 60000 entries) and instead uses only 1875 entries for training. Any possible fix?

I’ve implemented a neural network using tensor flow and it appears to be only running on 1/32 data points. I’ve then tried to following simple example to see if it was me: https://pythonprogramming.net/introduction-deep-learning-python-tensorflow-keras/ Even when using identical (copied and pasted) code I still get 1/32 of the training data being processed e.g. Epoch 3/3 1875/1875 … Read more

I have fine-tuned inception model with a new dataset and saved it as “.h5” model in Keras. now my goal is to run my model on android Tensorflow which accepts “.pb” extension only. question is that is there any library in Keras or tensorflow to do this conversion? I have seen this post so far : https://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html but can’t figure out yet.

I just installed the latest version of Tensorflow via `pip install tensorflow`

and whenever I run a program, I get the log message: