conv-neural-network

问题 I wanted to understand architectural intuition behind the differences of: tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)) and tf.keras.layers.Conv2D(32, (7,7), activation='relu', input_shape=(28, 28, 1)) Assuming, As kernel size increases, more complex feature-pattern matching can be performed in the convolution step. As feature size increases, a larger variance of smaller features can define a particular layer. How and when (if possible kindly give scenarios) do

问题 I tried different solutions but still facing the issue. Actually I am new in Ml/DL (python). In which case we face this error "Can not squeeze dim1, expected a dimension of 1, got 5"? Please help me here, what I am doing wrong here and what is correct Here is InvalidArgumentError Traceback (most recent call last) --------------------------------------------------------------------------- <ipython-input-9-0826122252c2> in <module>() 98 model.summary() 99 model.compile(loss='sparse_categorical

问题 I got the results by running the code provided in this link Neural Network – Predicting Values of Multiple Variables. I was able to compute losses accuracy etc. However, every time I run this code, I get a new result. Is it possible to get the same (consistent) result? 回答1: The code is full of random.randint() everywhere! Furthermore, the weights are most of the time randomly set aswell, and the batch_size also has an influence (although pretty minor) in the result. Y_train, X_test, X_train

问题 I got the results by running the code provided in this link Neural Network – Predicting Values of Multiple Variables. I was able to compute losses accuracy etc. However, every time I run this code, I get a new result. Is it possible to get the same (consistent) result? 回答1: The code is full of random.randint() everywhere! Furthermore, the weights are most of the time randomly set aswell, and the batch_size also has an influence (although pretty minor) in the result. Y_train, X_test, X_train

问题 I am training a cnn model with five classes using keras library. Using model.predict function i get prediction percentage of the classes. My problem is for a image which doesn't belong to these classes and completely irrelevant, the predict class still predicts the percentages according to the classes. How do I prevent it? How do I identify it as irrelevant? 回答1: I assume you are using a softmax activation on your last layer to generate the probabilities for each class. By definition, the sum

问题 I have the following model def get_model(): epochs = 100 learning_rate = 0.1 decay_rate = learning_rate / epochs inp = keras.Input(shape=(64, 101, 1), name="inputs") x = layers.Conv2D(128, kernel_size=(3, 3), strides=(3, 3), padding="same")(inp) x = layers.Conv2D(256, kernel_size=(3, 3), strides=(3, 3), padding="same")(x) x = layers.Flatten()(x) x = layers.Dense(150)(x) x = layers.Dense(150)(x) out1 = layers.Dense(40000, name="sf_vec")(x) out2 = layers.Dense(128, name="ls_weights")(x) model =

问题 I use PyTorch and transfer learning to train mobilenet_v2 based classifier. I use a batch of 20 images during training and my test accuracy is ~80%. I try to use the model with single image for individual prediction and output is a wrong class. At the same time if I will take a batch from my test dataset and insert my single image in it instead of element 0 it will have a correct prediction. Prediction 0 will be a correct class. So model works for a batch but not for an individual item. If I

问题 I have two different types of images (camera image and it's corresponding sketch). The goal of the network is to find the similarity between both images. The network consists of a single encoder and a single decoder. The motivation behind the single encoder-decoder is to share the weights between them. input_img = Input(shape=(img_width,img_height, channels)) def encoder(input_img): # Photo-Encoder Code pe = Conv2D(96, kernel_size=11, strides=(4,4), padding = 'SAME')(left_input) # (?, 64, 64,

问题 I have two different types of images (camera image and it's corresponding sketch). The goal of the network is to find the similarity between both images. The network consists of a single encoder and a single decoder. The motivation behind the single encoder-decoder is to share the weights between them. input_img = Input(shape=(img_width,img_height, channels)) def encoder(input_img): # Photo-Encoder Code pe = Conv2D(96, kernel_size=11, strides=(4,4), padding = 'SAME')(left_input) # (?, 64, 64,

问题 Similar to this question, I'm looking to have several image input layers that go through one larger CNN (e.g. XCeption minus dense layers), and then have the output of the one CNN across all images be concatenated into a dense layer. Is this possible with Keras or is it even possible to train a network from the ground-up with this architecture? I'm essentially looking to train a model that takes in a larger but fixed number of images per sample (i.e. 3+ image inputs with similar visual