message: Correct training command in micro_speech readme
diff: @@ -578,11 +578,9 @@ We strongly recommend trying this approach first.
  ### Use your local machine
  -You can use the following commands to train the model on your own machine.
  -
  -It may be easiest to run these commands in a
  -[TensorFlow Docker container](https://www.tensorflow.org/install/docker). A full
  -build may take a couple of hours.
  +You can use the following commands to train the model on your own machine. It
  +may be easiest to run these commands in a
  +[TensorFlow Docker container](https://www.tensorflow.org/install/docker).
  You must currently use the TensorFlow Nightly `pip` package. This version is
  confirmed to work:
  @@ -596,15 +594,13 @@ To begin training, run the following:
  ```
  python tensorflow/tensorflow/examples/speech_commands/train.py \
  --model_architecture=tiny_conv --window_stride=20 --preprocess=micro \
  ---wanted_words="yes,no" --silence_percentage=25 --unknown_percentage=25 \
  ---quantize=1 --verbosity=WARN --how_many_training_steps="15000,3000" \
  +--wanted_words="on,off" --silence_percentage=25 --unknown_percentage=25 \
  +--quantize=1 --verbosity=INFO --how_many_training_steps="15000,3000" \
  --learning_rate="0.001,0.0001" --summaries_dir=/tmp/retrain_logs \
  --data_dir=/tmp/speech_dataset --train_dir=/tmp/speech_commands_train
  ```
  -If you see a compiling error on older machines, try leaving out the `--copt`
  -arguments, they are just there to accelerate training on chips that support the
  -extensions. The training process is likely to take a couple of hours. Once it
  +The training process is likely to take a couple of hours. Once it
  has completed, the next step is to freeze the variables:
  ```
  @@ -619,7 +615,7 @@ The next step is to create a TensorFlow Lite file from the frozen graph:
  ```
  toco \
  --graph_def_file=/tmp/tiny_conv.pb --output_file=/tmp/tiny_conv.tflite \
  ---input_shapes=1,1960 --input_arrays=Reshape_1 --output_arrays='labels_softmax' \
  +--input_shapes=1,49,40,1 --input_arrays=Reshape_2 --output_arrays='labels_softmax' \
  --inference_type=QUANTIZED_UINT8 --mean_values=0 --std_dev_values=9.8077
  ```
  