Update dask to 0.15.0 will solve the issue update cmd: conda update dask input pip show dask will show follow message Name: dask Version: 0.15.0 Summary: Parallel PyData with Task Scheduling Home-page: http://github.com/dask/dask/ Author: Matthew Rocklin Author-email: [email protected] License: BSD Location: c:\anaconda3\lib\site-packages Requires:
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
If there’s a mask in your model, it’ll be propagated layer-by-layer and eventually applied to the loss. So if you’re padding and masking the sequences in a correct way, the loss on the padding placeholders would be ignored. Some Details: It’s a bit involved to explain the whole process, so I’ll just break it down … Read more
y_true and y_pred The tensor y_true is the true data (or target, ground truth) you pass to the fit method. It’s a conversion of the numpy array y_train into a tensor. The tensor y_pred is the data predicted (calculated, output) by your model. Usually, both y_true and y_pred have exactly the same shape. A few … Read more
I have the following understanding of this function keras.backend.function. I will explain it with the help of a code snippet from this. The part of code snippet is as follows final_conv_layer = get_output_layer(model, “conv5_3”) get_output = K.function([model.layers[0].input], [final_conv_layer.output, model.layers[-1].output]) [conv_outputs, predictions] = get_output([img]) In this code, there is a model from which conv5_3 layer is … 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
This would not be a standard approach to deal with unbalanced data. Nor do I think it would be really justified – you would be significantly changing the distributions of your classes, where the smaller class is now much less variable. The larger class would have rich variation, the smaller would be many similar images … Read more
Normally, the minority class will have a higher class weight. It’ll be better to use one_weight=0.89, zero_weight=0.11 (btw, you can use class_weight={0: 0.11, 1: 0.89}, as suggested in the comment). Under class imbalance, your model is seeing much more zeros than ones. It will also learn to predict more zeros than ones because the training … 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