im trying to deploy a keras project to heroku but pushing to the repository master branch seems to be problematic for me as the following error is reported ever
I am building a MLP using TensorFlow 2.0. I am plotting the learning curve and also using keras.evaluate on both training and test data to see how well it perfo
I'm using Google's Colab to run the Deep Learning codes from the Book " Deep Learning with python" by François Chollet. The 1st exercise is to use the mn
I trained my tensorflow model on images after convert it to BatchDataset IMG_size = 224 INPUT_SHAPE = [None, IMG_size, IMG_size, 3] # 4D input model.fit(
I have a simple code, which DOES work, for training a Keras model in Tensorflow using numpy arrays as features and labels. If I then wrap these numpy arrays usi
How to run keras.model.fit() in graph not with eager execution...?? I tried to run my model in graph by using tf.compat.v1.disable_eager_execution(), but the c
When I try to use a custom activation function in keras (2.2.5), I create a new activation function gelu. add it in activations.py : from . import backend as K
So about a week ago I posted this question: Issues running a Keras model with custom layers. The suggestion there was to try to make this question smaller and t
""" Defining two sets of inputs Input_A: input from the features Input_B: input from images my train_features has (792,192) shape my train_images has (792,28,28
Say I have a Custom Layer : class Custom_Layer(keras.layers.Layer): def __init__(self, **kwargs): self.w_0 = tf.Variable(tf.random_uniform_initializ
how can you save a keras model in 64bit format? This is able to 'put tensorflow' in 64bit 'mode' for the current runtime. But I've found that even just saving t
I am new to Machine Learning, and I followed this tutorial to implement LSTM model in Keras/Tensorflow: https://www.tensorflow.org/tutorials/structured_data/tim
I've created a multi-class image classifier using CNN. I am using the keras module specifically and I am using generators to fit and then predict 4 different cl
model.compile( optimizer= keras.optimizers.Adam(), loss= [keras.losses.SparseCategoricalCrossentropy(from_logits= True) ], metrices= ['accuracy']) mode
Tensorflow/Keras I have developed a CNN model to classify images as circle, triangle or square. However, my accuracy values have wide fluctuations. Is it someth
I am new to machine learning. I got the intermediate result of layer 31 of my CNN using the following code: conv2d = Model(inputs = self.model_ori.input, output
tf.keras.layers.TextVectorization layer maps text features to integer sequences, and since it can be added as a keras model layer it makes it easy to deploy the
I followed the tutorial here to try to train my model using CIFAR-100. But I'm getting this error. What do I do? ValueError: Data Params Error: The dataset labe
I'm currently implement the sequantial deep matching model (https://arxiv.org/abs/1909.00385) using tensorflow 2.3. And I included the preprocessing layer as pa
I want to distill knowledge from a teacher student to a student one, so I implemented a class named OCCSE that inherits from tf.keras.Model and accepts both tea
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