Pretrained_word_embeddings.py: inconsistency in `set_weights(weights)` vs Provided weight at layer "embedding_1"

I have downloaded the the raw code on pretrained_word_embeddings.py to try out. I can’t run it since the provided weight is not the same as the set weights, here is what I see, any way to fix this bug?

Using TensorFlow backend. Indexing word vectors. Found 400000 word vectors. Processing text dataset Found 19997 texts. Found 214873 unique tokens. Shape of data tensor: (19997, 1000) Shape of label tensor: (19997, 20) Preparing embedding matrix. Training model. Traceback (most recent call last): File “[the path]/pretrained_word_embeddings.py”, line 126, in <module> embedded_sequences = embedding_layer(sequence_input) File “/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py”, line 543, in call self.build(input_shapes[0]) File “/usr/local/lib/python3.5/dist-packages/keras/layers/embeddings.py”, line 101, in build self.set_weights(self.initial_weights) File “/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py”, line 966, in set_weights str(weights)[:50] + ‘…’) ValueError: You called set_weights(weights) on layer “embedding_1” with a weight list of length 1, but the layer was expecting 0 weights. Provided weights: [array([[ 0. , 0. , 0. , …

[SiWorgan]

Removing trainable=False from the Embedding layer and adding model.layers[1].trainable=False before it’s compiled worked for me.

[danielhers]

Turns out my problem wasn’t specifically with 1.2.0 or 1.1.2. I was passing both weights= and trainable=False to an Embedding layer, and they are incompatible.