tf.argmax(input, axis=None, name=None, dimension=None) Returns the index with the largest value across axis of a tensor. input is a Tensor and axis describes which axis of the input Tensor to reduce across. For vectors, use axis = 0. For your specific case let’s use two arrays and demonstrate this pred = np.array([[31, 23, 4, 24, … Read more
Based on what you said it sounds like you need a larger batch_size, and of course there are implications with that which could impact the steps_per_epoch and number of epochs. To solve for jumping-around A larger batch size will give you a better gradient and will help to prevent jumping around You may also want … Read more
In my understanding, the “backbone” refers to the feature extracting network which is used within the DeepLab architecture. This feature extractor is used to encode the network’s input into a certain feature representation. The DeepLab framework “wraps” functionalities around this feature extractor. By doing so, the feature extractor can be exchanged and a model can … Read more
It’s because of the batch normalization layers. In training phase, the batch is normalized w.r.t. its mean and variance. However, in testing phase, the batch is normalized w.r.t. the moving average of previously observed mean and variance. Now this is a problem when the number of observed batches is small (e.g., 5 in your example) … Read more
Change a = dataset[i:(i + look_back), 0] To a = dataset[i:(i + look_back), :] If you want the 3 features in your training data. Then use model.add(LSTM(4, input_shape=(look_back,3))) To specify that you have look_back time steps in your sequence, each with 3 features. It should run EDIT : Indeed, sklearn.preprocessing.MinMaxScaler()‘s function : inverse_transform() takes an … Read more
To make sure that you have “at least steps_per_epoch * epochs batches“, set the steps_per_epoch to steps_per_epoch = len(X_train)//batch_size validation_steps = len(X_test)//batch_size # if you have validation data You can see the maximum number of batches that model.fit() can take by the progress bar when the training interrupts: 5230/10000 [==============>……………] – ETA: 2:05:22 – loss: … Read more
The confusion is due to the meaning of axis in np.mean versus in BatchNormalization. When we take the mean along an axis, we collapse that dimension and preserve all other dimensions. In your example data.mean(axis=0) collapses the 0-axis, which is the vertical dimension of data. When we compute a BatchNormalization along an axis, we preserve … Read more
Since you trained your model on mini-batches, your input is a tensor of shape [batch_size, image_width, image_height, number_of_channels]. When predicting, you have to respect this shape even if you have only one image. Your input should be of shape: [1, image_width, image_height, number_of_channels]. You can do this in numpy easily. Let’s say you have a … Read more
TensorFlow doesn’t have first-class Tensor objects, meaning that there are no notion of Tensor in the underlying graph that’s executed by the runtime. Instead the graph consists of op nodes connected to each other, representing operations. An operation allocates memory for its outputs, which are available on endpoints :0, :1, etc, and you can think … Read more