deep-learning-with-python-notebooks
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fchollet
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID. https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_part05_gans.ipynb****
In the 2nd edition notebook of chapter 12 part 4, the latent space sampling layer is defined as the following class: ``` class Sampler(layers.Layer): def call(self, z_mean, z_log_var): batch_size =...
In _Deep Learning with Python_ 2e, @fchollet says: > Multi-hot encode your lists to turn them into vectors of 0s and 1s. This would mean, for instance, turning the sequence...
When i was about to execute `keras.utils.plot_model()` from a snippet in chapter - 07, i got the following error -- `You must install graphviz (see instructions at https://graphviz.gitlab.io/download/) for "plot_model"...
Chapter 8.2 using a convnet model with data augmentation and dropout to classify images of dogs and cats. The book says that the test accuracy is 83.5% but I tried...
I am triying to save the model used in chapter 12, text generation but cannot do so. How would i be able to save it? And load it back? Thanks
` encoder_inputs = keras.layers.Input(shape=(None,), dtype='int64', name='english') encoder_embed_outs = PositionalEmbedding(SEQ_LEN, MAX_VOCAB, EMBED_DIM)(encoder_inputs) encoder_transformer_outs = TransformerEncoder(num_heads=8, embed_dim=EMBED_DIM, dense_dim=2048)(encoder_embed_outs) #encoder_transformer_outs == (None, 80, 256) decoder_inputs = keras.layers.Input(shape=(None,), dtype='int64', name='spanish') decoder_embed_outs = PositionalEmbedding(SEQ_LEN, MAX_VOCAB,...
Like title says, also see here : https://stackoverflow.com/questions/76701617/the-following-arguments-are-not-supported-with-the-native-keras-format-opti/77342304#77342304 They work when I change the format to .tf and add `save_format="tf"` ... `callbacks = [ keras.callbacks.ModelCheckpoint("jena_lstm.tf", save_best_only=True, save_format="tf") ]`
The explanation of tfidf shown at page326 as below. `def tfidf(term, document, dataset):` `term_freq = document.count(term)` `doc_freq = math.log(sum(doc.count(term) for doc in dataset) + 1)` `return term_freq / doc_freq` Is...
