I am trying to run my biLSTM model on DGX station. Installed latest miniconda (4.3.30) on my suer account, and created a new conda environment as follows:
I tested the simple mnist_cnn.py (available online) code to make sure everything is ok. It works fine using GPU.
However, when I try my own model which is a simple BiLSTM, I see the model is not converging and does not return the results I get using CPU. It is very wired, and seems some libraries are not working fine.
l_lstm = Bidirectional(LSTM(EMBEDDING_DIM))(embedded_sequences)
preds = Dense(labels.shape, activation=‘softmax’)(l_lstm)
model = Model(sequence_input, preds)
adam = Adam(lr=0.0005, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
print("model fitting - Bidirectional LSTM") model.summary() model.fit(x_train, y_train, validation_data=(x_val, y_val), nb_epoch=n_epoch, batch_size=batch_s)
I like to know if anyone encountered this issue while running code on GPU?