conv-neural-network

问题 I am trying to train a Tensorflow Convolutional Neural Network, and I am always getting a cryptic error regardless of the environment in which i run the program. In Jupyter Notebook, the kernel simply dies. In Terminal, I get "Illegal Instruction: 4" with no Traceback. In Pycharm, I get: "Process finished with exit code 132 (interrupted by signal 4: SIGILL)". I have looked all over the Internet and I have not found any instance in which this particular error was thrown in this situation. I

问题 def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'): torch.save(state, filename) if is_best: shutil.copyfile(filename, 'model_best.pth.tar') save_checkpoint({ 'epoch': epoch + 1, 'arch': args.arch, 'state_dict': model.state_dict(), 'best_prec1': best_prec1, 'optimizer': optimizer.state_dict() }, is_best) I am saving my model like this. How can I load back the model so that I can use it in other places, like cnn visualization? This is how I am loading the model now: torch.load(

问题 I am working on a classification problem using CNN where my input image size is 64X64 and I want to use pretrained model such as VGG16 , COCO or any other. But the problem is input image size of pretrained model is 224X224 . How do I sort this issue. Is there any data augmentation way for input image size. If I resize my input image to 224X224 then there is very high chance of image will get blurred and that may impact the training. Please correct me if I am wrong. Another question is related

问题 I'm trying to train a model suggested by this research paper where I set half of the filters of a convolution layer to Gabor filters and the rest are random weights which are initialized by default. Normally, if I have to set a layer as not trainable, I set the trainable attribute as False . But here I have to freeze only half of the filters of a layer and I have no idea how to do so. Any help would be really appreciated. I'm using Keras with Tensorflow backend. 回答1: How about making two

问题 I'm trying to train a model suggested by this research paper where I set half of the filters of a convolution layer to Gabor filters and the rest are random weights which are initialized by default. Normally, if I have to set a layer as not trainable, I set the trainable attribute as False . But here I have to freeze only half of the filters of a layer and I have no idea how to do so. Any help would be really appreciated. I'm using Keras with Tensorflow backend. 回答1: How about making two

问题 With tf.nn.conv2d , you can perform a convolutional operation on a tensor. E.g. import tensorflow as tf x = tf.random.uniform(shape=(1, 224, 224, 3), dtype=tf.float32) filters = tf.random.uniform(shape=(1, 3, 3, 10)) tf.nn.conv2d(input=x, filters=filters, strides=1, padding='VALID') <tf.Tensor: shape=(1, 224, 222, 10), dtype=float32, numpy= array([[[[2.1705112, 1.2065555, 1.7674012, ..., 1.705754 , 1.3659815, 1.7028458], [2.0048866, 1.4835871, 1.2038497, ..., 1.8981357, 1.4605963, 2.148876 ],

问题 I am new to CNN and machine learning in general and have been trying to follow Image Classification tutorial of TensorFlow. Now, the Google Colab can be found here. I've followed the this official tutorial of TensorFlow. And I've changed it slightly so it saves the model as h5 instead of tf format so I can use the Keras' model.predict_classes . Now, I have the model trained and the model reloaded from the saved model alright. But I'm repeatedly getting list index out of range error whenever I

问题 I have input data (colored images) in the shape of (100, 64, 64, 3) and tried to train a CNN with 2 conv/pooling layers on it for binary classification. I keep encountering size mismatch error. Also attempted reshape the images into (-1, 3, 64, 64) size class SimpleCNN(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim, kernel_size): super(SimpleCNN, self).__init__() self.conv1 = nn.Conv2d(3, 10, kernel_size, padding=0) self.conv2 = nn.Conv2d(10, 20, kernel_size, padding=0) self

问题 I have a standard CNN for image classification, using the following generator to get the dataset: generator = validation_image_generator.flow_from_directory(batch_size=BATCH_SIZE, directory=val_dir, shuffle=False, target_size=(100,100), class_mode='categorical') I can easily get the predicted labels with: predictions = model.predict(dataset) Now I want to get the (original) true labels and images for all the predictions, in the same order as the predictions in order to compare them. I am sure

问题 I'm building this model: inputs = model.inputs[:2] layer_output = model.get_layer('Encoder-12-FeedForward-Norm').output input_layer= keras.layers.Input(shape=(SEQ_LEN,768))(layer_output) conv_layer= keras.layers.Conv1D(100, kernel_size=3, activation='relu', data_format='channels_first')(input_layer) maxpool_layer = keras.layers.MaxPooling1D(pool_size=4)(conv_layer) flat_layer= keras.layers.Flatten()(maxpool_layer) outputs = keras.layers.Dense(units=3, activation='softmax')(flat_layer) model =