Plz help how to write this code in pytorch

nlp
1

def build_combined_onehot(FLAGS, NUM_FILTERS, FILTER_LENGTH1, FILTER_LENGTH2):
XDinput = Input(shape=(FLAGS.max_smi_len, FLAGS.charsmiset_size))
XTinput = Input(shape=(FLAGS.max_seq_len, FLAGS.charseqset_size))

encode_smiles= Conv1D(filters=NUM_FILTERS, kernel_size=FILTER_LENGTH1,  activation='relu', padding='valid',  strides=1)(XDinput)
encode_smiles = Conv1D(filters=NUM_FILTERS*2, kernel_size=FILTER_LENGTH1,  activation='relu', padding='valid',  strides=1)(encode_smiles)
encode_smiles = Conv1D(filters=NUM_FILTERS*3, kernel_size=FILTER_LENGTH1,  activation='relu', padding='valid',  strides=1)(encode_smiles)
encode_smiles = GlobalMaxPooling1D()(encode_smiles) #pool_size=pool_length[i]


encode_protein = Conv1D(filters=NUM_FILTERS, kernel_size=FILTER_LENGTH2,  activation='relu', padding='valid',  strides=1)(XTinput)
encode_protein = Conv1D(filters=NUM_FILTERS*2, kernel_size=FILTER_LENGTH1,  activation='relu', padding='valid',  strides=1)(encode_protein)
encode_protein = Conv1D(filters=NUM_FILTERS*3, kernel_size=FILTER_LENGTH1,  activation='relu', padding='valid',  strides=1)(encode_protein)
encode_protein = GlobalMaxPooling1D()(encode_protein)



encode_interaction = keras.layers.concatenate([encode_smiles, encode_protein])
#encode_interaction = keras.layers.concatenate([encode_smiles, encode_protein], axis=-1) #merge.Add()([encode_smiles, encode_protein])

# Fully connected 
FC1 = Dense(1024, activation='relu')(encode_interaction)
FC2 = Dropout(0.1)(FC1)
FC2 = Dense(1024, activation='relu')(FC2)
FC2 = Dropout(0.1)(FC2)
FC2 = Dense(512, activation='relu')(FC2)


predictions = Dense(1, kernel_initializer='normal')(FC2) 

interactionModel = Model(inputs=[XDinput, XTinput], outputs=[predictions])
interactionModel.compile(optimizer='adam', loss='mean_squared_error', metrics=[cindex_score]) #, metrics=['cindex_score']


print(interactionModel.summary())
#plot_model(interactionModel, to_file='figures/build_combined_onehot.png')

return interactionModel
2

Have a look at the general approach of writing a model in this tutorial.
You could use the PyTorch equivalent methods for the ones you’ve posted, e.g.

  • Conv1D corresponds to nn.Conv1d in PyTorch
  • keras.layers.concatenate can be done via torch.cat((a, b), dim=your_dim) in the forward method
  • Dense corresponds to nn.Linear in PyTorch

Let us know, if you get stuck! :slight_smile: