Category "keras"

Keras installed through Conda breaks the Anaconda Prompt on Windows 10 [closed]

I have a Win10 OS, with Anaconda 3.6 installed, and a friend told me to install keras by using a specific conda command. Without reading any o

Is there any documentation about default weight initializer in Keras? [duplicate]

I just read about the Keras weight initializers in here. In the documentation, only different initializers has been introduced. Such as: mode

Keras - EarlyStopping based on user input

I am wondering if there is an easy way of creating a way of triggering early stopping in Keras based on user input rather than monitorization of any particular

How the TensorFlow Output Dense layers nodes mapping to labels?

I'm starting to study the tensorflow with the image classification sample which is the first sample on the tensorflow official document. It creates the Keras Se

How to Setup Adaptive Learning Rate in Keras

Below is my code: model = Sequential([ Dense(32, input_shape=(32,), activation = 'relu'), Dense(100, activation='relu'), Dense(65, input_shape=(65

How to see metrics of a loaded keras model?

I have trained a model with keras and saved it, can I see what the computed metrics during training were, after I load back the mode with keras.models import lo

How to avoid augmenting data in validation split of Keras ImageDataGenerator?

I'm using the following generator: datagen = ImageDataGenerator( fill_mode='nearest', cval=0, rescale=1. / 255, rotation_range=90, width_sh

UnimplementedError: Fused conv implementation does not support grouped convolutions for now

I am trying to build a CNN model to recognise human sketch using the TU-Berlin dataset. I downloaded the png zip file, imported the data to Google Colab and the

failed to install tensorflow on a EC2 instance Ubuntu 20.04

I have a flask application that I would like to run it on an EC2 instance and TensorFlow is needed cause it is image classification. However, after the necessar

How can I save an object containing keras models?

Here is my code skeleton: def build_model(x, y): model = tf.keras.models.Sequential() model.add(tf.keras.layers.Dense(1, activation='relu')) model.

ModuleNotFoundError: No module named 'tensorflow.python.keras.applications'

I am trying to import import tensorflow.python.keras.applications but it gives the bellow error: ModuleNotFoundError: No module named 'tensorflow.python.keras.

Variational AutoEncoder - TypeError

I am trying to implement a VAE for MNIST using convolutional layers using TensorFlow-2.6 and Python-3.9. The code I have is: # Specify latent space dimensions-

How is the smooth dice loss differentiable?

I am training a U-Net in keras by minimizing the dice_loss function that is popularly used for this problem: adapted from here and here def dsc(y_true, y_pred)

Keras ModelCheckpoint val_loss decreases but says it doesn't

I use a ModelCheckPoint in Keras to save only the best models. Although, I see the val_loss decreasing the ModelCheckPoint says; No. Any ideas? checkpoint = Mod

Siamese Network for binary classification with pre-encoded inputs

I want to train a Siamese Network to compare vectors for similarity. My dataset consist of pairs of vectors and a target column with "1" if they are the same an

How to run tensorflow inference for multiple models on GPU in parallel?

Do you know any elegant way to do inference on 2 python processes with 1 GPU tensorflow? Suppose I have 2 processes, first one is classifying cats/dogs, 2nd on

How can I run tensorflow without GPU?

My system has a GPU. When I run Tensorflow on it, TF automatically detects GPU and starts running the thread on the GPU. How can I change this? I.e. how can I r

Correct Implementation of Dice Loss in Tensorflow / Keras

I've been trying to experiment with Region Based: Dice Loss but there have been a lot of variations on the internet to a varying degree that I could not find tw

AttributeError: module 'keras.utils' has no attribute 'get_file'

When I'm trying to implement the following code from keras_segmentation.models.segnet import resnet50_segnet from keras_segmentation.predict import model_from_c

LSTM is Showing very low accuracy and large loss

I am applying LSTM on a dataset that has 53699 entries for the training set and 23014 entries for the test set. The shape of the input training set is (53699,4)