I am trying to write a simple 1 dimensional convolution with a regression (1 dimensional float) output. model = Sequential() model.add(Conv1D(filters=1, kernel_
im trying to re train the mobilnet model with my own dataset and when I am trying to fit the model I get these error. mobile=tf.keras.applications.mobilenet.Mob
I am trying to use Tensorflow and Keras for a prediction model. I first read in my dataset that has shape (7709, 58), then normalize it: normalizer = tf.keras.l
currently I'am training my Word2Vec + LSTM for Twitter sentiment analysis. I use the pre-trained GoogleNewsVectorNegative300 word embedding. The reason I used t
I am working with TensorFlow Federated framework and designed a keras model for a binary classification problem. I defined the iterative process with tff.learni
I am a newbie in some sequential models in Tensorflow with Python. I have a transformation sequential model like the one below. It applies randomly to a given i
I have read in previous posts that using the TimeDistributed parameter in the LSTM model does not change the performance or how the layers work (It only gives a
I am trying to reproduce my code from online Jupyter Notebook (COURSERA) to my own local environment （Anaconda 3 Jupyter with CUDA installed) All Codes a
i am new to python and i am trying to create a model that can measure how similar movies are based on the movies description,the steps i followed so far are: 1.
I am trying to print all the 1290 parameters in dense_1 layer, but model.get_weights()[7] only show 10 parameters. How could I print all the 1290 parameters of
I'm trying to deploy my machine learning model to AWS Lambda function. I trained my model locally with Keras and got .h5 file. I zipped Tensorflow and Keras mod
How can I make this DNN model in tensorflow? 31 neurons in the first, 10 in the second, 5 in the third hidden layer, and 2 neurons in the output layer. The acti
I am getting the same prediction for different inputs. I am trying to use a regressional neural network. I want to predict values instead of class using neural
I'm working on a binary semantic segmentation problem. I built an UNet model with MobileNetV2 backbone. Here is my model code: def upsample(filters, size, apply
I want to convert this below RNN into bidirectional RNN, how can I do that? #Call the function and compile the model. model = RNN() model.summary() model.compil
I am dealing with a binary classification problem that feeds a network with two inputs (images), model_vgg16_conv = VGG16(weights='imagenet', include_top=False)
I know that how to generate next word in keras with lstm but how to predict previous word for example If you have two words like "car" and "running" then It sho
I am testing outcomes of tf.keras.losses.CategoricalCrossEntropy, and it gives me values different from the definition. My understanding of cross entropy is: d
from tensorflow.keras.applications import VGG16 pre_trained_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) model = Sequential
I have to do grid search on my DNN. But I am getting an error on GridSearchCV function. Here is the code for creating and compiling the model I used and also wh