An input function using data already in memory for iris predictions

tf_memory_input_function.py

# Let create a memory dataset for prediction.
# We've taken the first 3 examples in FILE_TEST.
prediction_input = [[5.9, 3.0, 4.2, 1.5],  # -> 1, Iris Versicolor
                    [6.9, 3.1, 5.4, 2.1],  # -> 2, Iris Virginica
                    [5.1, 3.3, 1.7, 0.5]]  # -> 0, Iris Sentosa
def new_input_fn():
   def decode(x):
       x = tf.split(x, 4) # Need to split into our 4 features
       # When predicting, we don't need (or have) any labels
       return dict(zip(feature_names, x)) # Then build a dict from them

   # The from_tensor_slices function will use a memory structure as input
   dataset = tf.data.Dataset.from_tensor_slices(prediction_input)
   dataset = dataset.map(decode)
   iterator = dataset.make_one_shot_iterator()
   next_feature_batch = iterator.get_next()
   return next_feature_batch, None # In prediction, we have no labels

# Predict all our prediction_input
predict_results = classifier.predict(input_fn=new_input_fn)

# Print results
print("Predictions on memory data")
for idx, prediction in enumerate(predict_results):
   type = prediction["class_ids"][0] # Get the predicted class (index)
   if type == 0:
       print("I think: {}, is Iris Sentosa".format(prediction_input[idx]))
   elif type == 1:
       print("I think: {}, is Iris Versicolor".format(prediction_input[idx]))
   else:
       print("I think: {}, is Iris Virginica".format(prediction_input[idx])