I’ve added built-in support for keras in tqdm so you could use it instead (pip install “tqdm>=4.41.0”): from tqdm.keras import TqdmCallback … model.fit(…, verbose=0, callbacks=[TqdmCallback(verbose=2)]) This turns off keras‘ progress (verbose=0), and uses tqdm instead. For the callback, verbose=2 means separate progressbars for epochs and batches. 1 means clear batch bars when done. 0 means … Read more
The difference lies in when you pass as x data that is larger than one batch. predict will go through all the data, batch by batch, predicting labels. It thus internally does the splitting in batches and feeding one batch at a time. predict_on_batch, on the other hand, assumes that the data you pass in … Read more
Each layer has its own default value for initializing the weights. For most of the layers, such as Dense, convolution and RNN layers, the default kernel initializer is ‘glorot_uniform’ and the default bias intializer is ‘zeros’ (you can find this by going to the related section for each layer in the documentation; for example here … Read more
*Edited to include relevant code to implement permutation importance. I answered a similar question at Feature Importance Chart in neural network using Keras in Python. It does implement what Teque5 mentioned above, namely shuffling the variable among your sample or permutation importance using the ELI5 package. from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor import eli5 from eli5.sklearn … Read more
The first problem is with the LSTM input_shape. input_shape = (20,85,1). From the doc: https://keras.io/layers/recurrent/ LSTM layer expects 3D tensor with shape (batch_size, timesteps, input_dim). model.add(tf.keras.layers.Dense(nb_classes, activation=’softmax’)) – this suggets you’re doing a multi-class classification. So, you need your y_train and y_test have to be one-hot-encoded. That means they must have dimension (number_of_samples, 3), where … Read more
So I tried link above which you have mentioned teachable machine As it turns out model you have exported is from tensorflow.keras and not directly from keras API. These two are different. So while loading it might be using tf.ragged tensors that might not be compatible with keras API. Soulution to your issue: Don’t import … Read more
With only 3 line of code… scaler = StandardScaler() X_train = scaler.fit_transform(X_train.reshape(-1, X_train.shape[-1])).reshape(X_train.shape) X_test = scaler.transform(X_test.reshape(-1, X_test.shape[-1])).reshape(X_test.shape)
In practice even in NLP you see that RNNs and CNNs are often competitive. Here’s a 2017 review paper that shows this in more detail. In theory it might be the case that RNNs can handle the full complexity and sequential nature of language better but in practice the bigger obstacle is usually properly training … Read more
You can convert it to a list with list(ds) and then recompile it as a normal Dataset with tf.data.Dataset.from_tensor_slices(list(ds)). From there your nightmare begins again but at least it’s a nightmare that other people have had before. Note that for more complex datasets (e.g. nested dictionaries) you will need more preprocessing after calling list(ds), but … Read more
In the context of autoencoders the input and output of the model is the same. So, if the input values are in the range [0,1] then it is acceptable to use sigmoid as the activation function of last layer. Otherwise, you need to use an appropriate activation function for the last layer (e.g. linear which … Read more