I have code that trains a DNN network. I don't want to train this network every time, because it uses too much time. How can I save the model? def train_model(filename, validation_ratio=0.): # define model to be trained columns = [tf.contrib.layers.real_valued_column(str(col), dtype=tf.int8) for col in FEATURE_COLS] classifier = tf.contrib.learn.DNNClassifier( feature_columns=columns, hidden_units=[100, 100], n_classes=N_LABELS, dropout=0.3) # load and split data print( 'Loading training data.') data = load_batch(filename) overall_size = data.shape[0] learn_size = int(overall_size * (1 - validation_ratio)) learn, validation = np.array_split(data, [learn_size]) print( 'Finished loading data. Samples count = {}'.format(overall_size)) # learning print( 'Training using batch of size {}'.format(learn_size)) classifier.fit(input_fn=lambda: pipeline(learn), steps=learn_size) if validation_ratio > 0: validate_model(classifier, learn, validation) return classifier After running this function, I get a DNNClassifier which I want to save. Continue reading...
