Fix sample generation for scalar inputs

[nsfinkelstein]

Sample generation with scalar inputs is broken. When it is reshaped in predict_proba, it is given too few dimensions. This pull requests corrects the number of dimensions.

Here is a simple reproducible example.

Set the value of wavenet_params.json to the following:

{
    "filter_width": 2,
    "sample_rate": 16000,
    "dilations": [1, 2, 4, 8],
    "residual_channels": 32,
    "dilation_channels": 32,
    "quantization_channels": 256,
    "skip_channels": 512,
    "use_biases": true,
    "scalar_input": true,
    "initial_filter_width": 2
}

Training:

$ python train.py --data_dir train_data --silence_threshold 0.0001
Using default logdir: ./logdir/train/2017-09-18T13-42-07
files length: 100
step 0 - loss = 5.537, (1.306 sec/step)
Storing checkpoint to ./logdir/train/2017-09-18T13-42-07 ...files length: 100

Generating (before the fix):

$ python generate.py logdir/train/2017-09-18T13-42-07/model.ckpt-0 --fast_generation false

Traceback (most recent call last):
  File "/home/noam/code/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py", line 671, in _call_cpp_shape_fn_impl
    input_tensors_as_shapes, status)
  File "/home/noam/code/anaconda/lib/python3.6/contextlib.py", line 89, in __exit__
    next(self.gen)
  File "/home/noam/code/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape must be rank 4 but is rank 3 for 'wavenet_1/causal_layer/causal_conv/conv1d/Conv2D' (op: 'Conv2D') with input shapes: [?,1,1], [1,2,1,32].

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "generate.py", line 279, in <module>
    main()
  File "generate.py", line 163, in main
    next_sample = net.predict_proba(samples, args.gc_id)
  File "/home/noam/code/heartrate-wavenet/wavenet/model.py", line 586, in predict_proba
    raw_output = self._create_network(encoded, gc_embedding)
  File "/home/noam/code/heartrate-wavenet/wavenet/model.py", line 406, in _create_network
    current_layer = self._create_causal_layer(current_layer)
  File "/home/noam/code/heartrate-wavenet/wavenet/model.py", line 243, in _create_causal_layer
    return causal_conv(input_batch, weights_filter, 1)
  File "/home/noam/code/heartrate-wavenet/wavenet/ops.py", line 55, in causal_conv
    restored = tf.nn.conv1d(value, filter_, stride=1, padding='VALID')
  File "/home/noam/code/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 2010, in conv1d
    data_format=data_format)
  File "/home/noam/code/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 399, in conv2d
    data_format=data_format, name=name)
  File "/home/noam/code/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "/home/noam/code/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2508, in create_op
    set_shapes_for_outputs(ret)
  File "/home/noam/code/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1873, in set_shapes_for_outputs
    shapes = shape_func(op)
  File "/home/noam/code/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1823, in call_with_requiring
    return call_cpp_shape_fn(op, require_shape_fn=True)
  File "/home/noam/code/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py", line 610, in call_cpp_shape_fn
    debug_python_shape_fn, require_shape_fn)
  File "/home/noam/code/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/common_shapes.py", line 676, in _call_cpp_shape_fn_impl
    raise ValueError(err.message)
ValueError: Shape must be rank 4 but is rank 3 for 'wavenet_1/causal_layer/causal_conv/conv1d/Conv2D' (op: 'Conv2D') with input shapes: [?,1,1], [1,2,1,32].

Generating (after the fix):

$ python generate.py logdir/train/2017-09-18T13-42-07/model.ckpt-0 --fast_generation false
Restoring model from logdir/train/2017-09-18T13-42-07/model.ckpt-0
Sample 15506/16000
Finished generating. The result can be viewed in TensorBoard.