message: Simplify first-time conditional, allowing SVDF model to run successfully in
  android demo app.
diff: @@ -530,6 +530,10 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings,
  shape=[num_filters, batch, input_time_size],
  trainable=False,
  name='runtime-memory')
  + first_time_flag = tf.get_variable(
  + name="first_time_flag",
  + dtype=tf.int32,
  + initializer=1)
  # Determine the number of new frames in the input, such that we only operate
  # on those. For training we do not use the memory, and thus use all frames
  # provided in the input.
  @@ -540,9 +544,10 @@ def create_low_latency_svdf_model(fingerprint_input, model_settings,
  window_stride_ms = int(model_settings['window_stride_samples'] * 1000 /
  model_settings['sample_rate'])
  num_new_frames = tf.cond(
  - tf.equal(tf.count_nonzero(memory), 0),
  + tf.equal(first_time_flag, 1),
  lambda: input_time_size,
  lambda: int(runtime_settings['clip_stride_ms'] / window_stride_ms))
  + first_time_flag = 0
  new_fingerprint_input = fingerprint_input[
  :, -num_new_frames*input_frequency_size:]
  # Expand to add input channels dimension.
  