How do I correctly use Keras Embedding layer?

Question

I have written the following multi-input Keras TensorFlow model:

CHARPROTLEN = 25 #size of vocab
CHARCANSMILEN = 62 #size of vocab

protein_input = Input(shape=(train_protein.shape[1:]))
compound_input = Input(shape=(train_smile.shape[1:]))

#protein layers
x = Embedding(input_dim=CHARPROTLEN+1,output_dim=128, input_length=maximum_amino_acid_sequence_length) (protein_input)
x = Conv1D(filters=32, padding="valid", activation="relu", strides=1, kernel_size=4)(x)
x = Conv1D(filters=64, padding="valid", activation="relu", strides=1, kernel_size=8)(x)
x = Conv1D(filters=96, padding="valid", activation="relu", strides=1, kernel_size=12)(x)
final_protein = GlobalMaxPooling1D()(x)

#compound layers
y = Embedding(input_dim=CHARCANSMISET+1,output_dim=128, input_length=maximum_SMILES_length) (compound_input)
y = Conv1D(filters=32, padding="valid", activation="relu", strides=1, kernel_size=4)(y)
y = Conv1D(filters=64, padding="valid", activation="relu", strides=1, kernel_size=6)(y)
y = Conv1D(filters=96, padding="valid", activation="relu", strides=1, kernel_size=8)(y)
final_compound = GlobalMaxPooling1D()(y)

join = tf.keras.layers.concatenate([final_protein, final_compound], axis=-1)


x = Dense(1024, activation="relu")(join)
x = Dropout(0.1)(x)
x = Dense(1024, activation='relu')(x)
x = Dropout(0.1)(x)
x = Dense(512, activation='relu')(x)

predictions = Dense(1,kernel_initializer='normal')(x)

model = Model(inputs=[protein_input, compound_input], outputs=[predictions])

The inputs have the following shapes:

train_protein.shape
TensorShape([5411, 1500, 1])

train_smile.shape
TensorShape([5411, 100, 1])

I get the following error message:

ValueError: One of the dimensions in the output is <= 0 due to downsampling in conv1d. Consider increasing the input size. Received input shape [None, 1500, 1, 128] which would produce output shape with a zero or negative value in a dimension.

Is this due to the Embedding layer having the incorrect output_dim? How do I correct this? Thanks.

Answer 1

A Conv1D layer requires the input shape (batch_size, timesteps, features), which train_protein and train_smile already have. For example, train_protein consists of 5411 samples, where each sample has 1500 timesteps, and each timestep one feature. Applying an Embedding layer to them results in adding an additional dimension, which Conv1D layers cannot work with.

You have two options. You either leave out the Embedding layer altogether and feed your inputs directly to the Conv1D layers, or you reshape your data to be (5411, 1500) for train_protein and (5411, 100) for train_smile. You can use tf.reshape, tf.squeeze, or tf.keras.layers.Reshape to reshape the data. Afterwards you can use the Embedding layer as planned. And note that output_dim determines the n-dimensional vector to which each timestep will be mapped. See also this and this.