for training_condition in ['context', 'context_stress', 'context_phonology']:

# load data set with dimensionality-reduced word embeddings obtained from bimodal auto encoder

# trained in 'train_bimodal_auto_encoder.py'

data_set = load_bimodal_data_set(training_condition)

# softmax model for predicting words from the right context

softmax = SoftmaxPredictor(data_set=data_set,

n_next_words=2000,

learning_rate=0.008,

input_size=500)

# train the model

softmax.train()

# get word embeddings

embeddings = softmax.embeddings_over_epochs[-1]

# write predicted categories to file -- files can be used for additional processing, e.g. significance testing

# on predictions made at two different stages in the training process

write_preds_to_file(embeddings, '10-NN', '%s_10-NN_after_stage2' % training_condition)

# append 10-NN results obtained from word embeddings to file

micro_f1, macro_f1, classification_report = get_f1_and_classification_report(embeddings, '10-NN')

results_to_disk(micro_f1, macro_f1, classification_report, epoch=softmax.epochs,

condition=training_condition, training_stage='AFTER STAGE 2', newfile=False)

# plot word embeddings, reduced to two dimensions via the T-SNE algorithm

plot_2D_embeddings(embeddings, training_condition, training_stage='after_stage_2')

# plot accuracy over all training epochs

plot_metric(plot_name='%s_softmax' % training_condition, plot_type='micro_f1_over_epochs',

ys=softmax.f1_over_epochs, label='micro_f1')

# plot training error over all training epochs

plot_metric(plot_name='%s_softmax' % training_condition, plot_type='training_error_over_epochs',