tensorflow

tf.keras.utils.get_file error: TypeError: '<' not supported between instances of 'int' and 'NoneType' Question: everyone, Recently I start to program with tensorflow 2.10.0, have the following codes in my ipynb file (Jupyter Notebook file): if not data_dir.exists(): tf.keras.utils.get_file(‘free-spoken-digit-dataset-master.zip’,origin="https://codeload.github.com/Jakobovski/free-spoken-digit-dataset/zip/refs/heads/master",extract=True,cache_dir=’.’,cache_subdir=’data’) I want to download the file free-spoken-digit-dataset-master.zip from the URL https://codeload.github.com/Jakobovski/free-spoken-digit-dataset/zip/refs/heads/master, after running the codes the following error …

Loss is Nan for SegFormer vision transformer trained on BDD10k Question: I’m trying to implement a SegFormer pretrained with a mit-b0 model to perform semantic segmentation on images obtained from the bdd100k dataset. Specifically, semantic segmentation has masks for only a subset of the 100k images, being 10k with appropriate masks for segmentation where the …

Can i initialize optimizer before changing the last layer of my model Question: Say I want to change the last layer of my code but my optimizer is defined on the top of my scripts, what is a better practise? batch_size = 8 learning_rate = 2e-4 num_epochs = 100 cnn = models.resnet18(weights=’DEFAULT’) loss_func = nn.CrossEntropyLoss() …

Shapes for training data and labels Question: I am trying to train a convolutional neural network using Conv1D and sparse_categorical_crossentropy as a loss function but I keep having problems related to the shapes of the data. Here is the network: model=models.Sequential() model=tf.keras.models.Sequential([ tf.keras.layers.Conv1D(16, 24, input_shape=(1000, 4), activation="relu"), tf.keras.layers.MaxPooling1D(pool_size=24, strides=24), tf.keras.layers.Conv1D(8, 8, activation="relu"), tf.keras.layers.MaxPooling1D(pool_size=8, strides=8), tf.keras.layers.Flatten(), …

WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,recall Question: I have the error in the title when running the code below: def create_model(learning_rate, dropout, l2_lambda): model = Sequential() model.add(Dense(128,input_dim=X_train.shape[1], activation=’relu’, kernel_regularizer=regularizers.l2(l2_lambda))) model.add(BatchNormalization()) model.add(Dropout(dropout)) model.add(Dense(64, activation=’relu’, kernel_regularizer=regularizers.l2(l2_lambda))) model.add(Dropout(dropout)) model.add(Dense(1, activation=’sigmoid’)) optimizer = Adam(learning_rate=learning_rate) model.compile(loss=’binary_crossentropy’, optimizer=optimizer, metrics=[‘Recall’]) return model early_stopping = …

why my tf.tensor_scatter_nd_add can't do the same as torch scatter_add_ Question: new_means = tf.tensor_scatter_nd_add(new_means, indices=repeat(buckets, "n -> n d", d=dim), updates=samples) assumpt new_means.shape=[3, 4], indices.shape=[4, 4] and updates.sahpe=[4, 4]. the above code return err : Inner dimensions of output shape must match inner dimensions of updates shape. Output: [3,4] updates: [4,4]. even when I set …

How does TensorFlow perform algorithms so fast? Question: I wanted to solve a linear regression problem by creating by myself the Adam optimization algorithm and performing it on my dataset. However, the problem is solved with an acceptable loss in around 100 epochs, while a tenth than my loss is computed by TensorFlow in just …

x and y must have same first dimension, but have shapes (9,) and (4,) Question: y_pred = fig.predict(x.reshape(1, -1)).reshape(-1) colors_dark = ["#1F1FIF", "#313131", "#636363", "#AEAEAE", "#DADADA"] colors_red = ["#331313", "#582626", "#9E1717", "#D35151", "#E9B4B4"] colors_green=[ "# 01411C", "#4B6F44", "#4F7942", "#74C365", "#D0F0C0"] filterwarnings("ignore") epochs =[ i for i in range(9)] fig, ax = plt.subplots(1, 2,figsize=(14,7)) train_acc1 = …

tf keras autokeras with early stopping returns empty history Question: I am trying different models for the same dataset, being autokeras.ImageClassifier one of them. First I go for img_size = (100,120,3) train_dataset = get_dataset(x_train, y_train, img_size[:-1], 128) valid_dataset = get_dataset(x_valid, y_valid, img_size[:-1], 128) test_dataset = get_dataset(x_test, y_test, img_size[:-1], 128) For getting the dataset with a …

On windows11, nvcc cannot show the correct version of CUDA Question: The version I originally installed is 11.6. To match the tensorflow2.11.0, I had to install the v11.2 of CUDA, during which I first uinstall the newer version of CUDA(every thing about it) and then install the earlier version that is 11.2. However, after the …