ValueError: Layer sequential_20 expects 1 inputs, but it received 2 input tensors
it helped me when I changed: validation_data=[X_val, y_val] into validation_data=(X_val, y_val) Actually still wonder why?
keras
keras tensorboard: plot train and validation scalars in a same figure
To handle the validation logs with a separate writer, you can write a custom callback that wraps around the original TensorBoard methods. import os import tensorflow as tf from keras.callbacks import TensorBoard class TrainValTensorBoard(TensorBoard): def __init__(self, log_dir=”./logs”, **kwargs): # Make the original `TensorBoard` log to a subdirectory ‘training’ training_log_dir = os.path.join(log_dir, ‘training’) super(TrainValTensorBoard, self).__init__(training_log_dir, **kwargs) … Read more
Choosing number of Steps per Epoch
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
AttributeError: ‘module’ object has no attribute ‘computation’
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: mrocklin@gmail.com License: BSD Location: c:\anaconda3\lib\site-packages Requires:
ResNet: 100% accuracy during training, but 33% prediction accuracy with the same data
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
How to work with multiple inputs for LSTM in Keras?
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
How do I mask a loss function in Keras with the TensorFlow backend?
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
What is y_true and y_pred when creating a custom metric in Keras?
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
What’s the purpose of keras.backend.function()
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
tensorflow:Your input ran out of data
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