Train 1-dimensional Convolution Network

Train 1-dimensional Convolution Network using keras on the given dataset

Arguments

data	the sentiment140 train dataset with text for text of the tweet and polarity for polarity.
max_words	Maximum number of words to consider using word frequency measure.
maxlen	Maximum length of a sequence.
embedding_dim	Output dimension of the embedding layer.
epochs	Number of epochs to run the training for.
batch_size	Batch Size for model fitting.
validation_split	Split ratio for validation
conv1d_filters	Number of filters i.e. output dimension for convolution layers.
conv1d_kernel_size	Window size for convolution layers.
conv1d_pool_size	Pool size for max pooling.
seed	Seed for shuffling training data.
model_save_path	File path location for saving model.

Value

plot of the training operation showing train vs validation loss and accuracy.

Examples

# NOT RUN {
  data(sentiment140_train)
  train_conv_1d(model_save_path = "./train_no_glove_conv_1d.h5")
# }