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
Here I’m answering to OP’s topic question rather than his exact problem. I’m doing this as the question shows up in the top when I google the topic problem. You can implement a custom metric in two ways. As mentioned in Keras docu. import keras.backend as K def mean_pred(y_true, y_pred): return K.mean(y_pred) model.compile(optimizer=”sgd”, loss=”binary_crossentropy”, metrics=[‘accuracy’, … Read more
The following function allows you to insert a new layer before, after or to replace each layer in the original model whose name matches a regular expression, including non-sequential models such as DenseNet or ResNet. import re from keras.models import Model def insert_layer_nonseq(model, layer_regex, insert_layer_factory, insert_layer_name=None, position=’after’): # Auxiliary dictionary to describe the network graph … Read more
As it’s quite difficult to clarify where the problem is, I created a toy example from your code, and it seems to work alright. import numpy as np from numpy.testing import assert_allclose from keras.models import Sequential, load_model from keras.layers import LSTM, Dropout, Dense from keras.callbacks import ModelCheckpoint vec_size = 100 n_units = 10 x_train = … Read more
When saving a Lambda layer, the arguments passed in will also be saved. In this case, it contains two tf.Tensors. It seems that Keras does not support serializing tf.Tensor in the model config right now. However, numpy arrays can be serialized without problem. So instead of passing tf.Tensor in arguments, you can pass in numpy … Read more
I faced the same error and tried to test everything with no value, but I heard that you have to make the number of folders in the dataset the SAME as the one in Dense. I don’t know if this will solve your specific bug or not but try this with your code: vgg_model.add(tf.keras.layers.Dense(10, activation … Read more
Yes, there is! custom_objects expects the exact function that you used as loss function (the inner one in your case): model = load_model(modelFile, custom_objects={ ‘loss’: penalized_loss(noise) }) Unfortunately keras won’t store in the model the value of noise, so you need to feed it to the load_model function manually.
We can do that easily in tf. keras using its awesome Functional API. Here we will walk you through how to build multi-out with a different type (classification and regression) using Functional API. According to your last diagram, you need one input model and three outputs of different types. To demonstrate, we will use MNIST … Read more
If you get errors about h5py submodules, try to use collect_submodules function to add them all to hidden_imports. You probably noticed a file called myscript.spec generated by a pyinstaller. Inside this file is an instruction on how to build you script (and it’s just a python code too!). So try to edit this myscript.spec like … Read more
Don’t let the name confuse you. The layer tf.keras.layers.Conv1D needs the following shape: (time_steps, features). If your dataset is made of 10,000 samples with each sample having 64 values, then your data has the shape (10000, 64), which is not directly applicable to the tf.keras.layers.Conv1D layer. You are missing the time_steps dimension. What you can … Read more