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def compare_rl_and_gail(metric, window, ylabel=None, xlim=None, fs=15, ncol=2):
    df1 = df_rl[df_rl['config.rp_weight'] == 1.0]
    df2 = df_rl[df_rl['config.rp_weight'] == 0.0]
    df3 = df_gail[df_gail['config.rmt.top_k_percent'] == 0.]
    df4 = df_gail[df_gail['config.rmt.top_k_percent'] == 0.25]
    df5 = df_gail[df_gail['config.rmt.top_k_percent'] == 0.5]
    df6 = df_gail[df_gail['config.rmt.top_k_percent'] == 0.75]
    for i, df in enumerate([df1, df2, df3, df4, df5, df6]):
        value = 100 * pandas.rolling_mean(average_value(df, metric), window)
        print(value.iloc[-1])

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def plot_key_alphas(data, softmax=True, fixed_axis=True):
  f, axes = pyplot.subplots(1, 3)
  f.set_size_inches((15, 2))
  for k in range(3):
    arr = numpy.array(data[k])
    arr = arr[:, [4, 5, 7]]
    if softmax:
      arr = numpy.exp(arr) / numpy.exp(arr).sum(axis=1)[:, None]
    else:
      arr = arr / arr.sum(axis=1)[:, None]

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def load_alpha_data(log_file):
  data = [[], [], []]
  with open(log_file) as log:
    for line in log:
      if line.startswith('data'):
        num = int(line[5])
        row = [float(x) for x in line[7:].split()]
        data[num].append(row)
  return data

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ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-2-84c8d19d2bb4> in <module>()
----> 1 from shapeworld import Dataset
      2 
      3 dataset = Dataset.create(dtype='agreement', name='multishape')
      4 generated = dataset.generate(n=128, mode='train', include_model=True)
      5 

~/Dist/ShapeWorld/shapeworld/__init__.py in <module>()
----> 1 from shapeworld.dataset import Dataset

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Traceback (most recent call last):

  File "/u/bahdanau/.conda/envs/py36clone/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2910, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)

  File "<ipython-input-2-84c8d19d2bb4>", line 1, in <module>
    from shapeworld import Dataset

  File "/u/bahdanau/Dist/ShapeWorld/shapeworld/__init__.py", line 1, in <module>
    from shapeworld.dataset import Dataset

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Question: "Is there a cyan shiny thing of the same size as the block?"
No
Is there a cyan shiny thing of the same size as the block? no
FiLM layer 0
(size, 6.938) (the, 5.774) (same, 3.317) (there, 3.068) (as, 3.067) (cyan, 2.567) (Is, 0.406)
FiLM layer 1
(the, 6.826) (same, 4.392) (as, 4.130) (size, 3.484) (cyan, 2.280) (there, 2.038) (block, 0.408) (that, 0.262) (a, 0.135) (any, 0.112)
FiLM layer 2
(the, 9.060) (size, 4.601) (as, 3.907) (same, 2.995) (cyan, 1.927) (there, 1.653) (of, 0.022)
FiLM layer 3

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Using cuDNN version 5105 on context None
Preallocating 14677/16308 Mb (0.900000) on cuda
Mapped name None to device cuda: Tesla P100-SXM2-16GB (0000:0A:00.0)
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
/Tmp/lisa/os_v5/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:913: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))

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import tensorflow as tf
import numpy
import time

# The computation graph (in fact just an LSTM language model)
batch_size = 100
vocab_size = 50000
dim = 512

inputs = tf.placeholder(tf.int32, [batch_size, None],

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init_step = tf.constant(1)
init_states = tf.TensorArray(tf.float32, size=n_steps, clear_after_read=False)
init_states = init_states.write(0, tf.ones((128, 128)))
def should_continue(step, states):
    return step < n_steps
def iteration(step, states):
    previous_states = states.gather(tf.range(step))
    return step + 1, states.write(step, tf.reduce_sum(previous_states, 0) + 1)
_, states = tf.while_loop(should_continue, iteration, [init_step, init_states])

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Function profiling
==================
  Message: /u/bahdanau/Dist/fully-neural-lvsr/libs/blocks/blocks/monitoring/evaluators.py:181
  Time in 100 calls to Function.__call__: 2.154827e-03s
  Time in Function.fn.__call__: 9.248257e-04s (42.919%)
  Total compile time: 4.125585e+00s
    Number of Apply nodes: 0
    Theano Optimizer time: 6.079912e-03s
       Theano validate time: 0.000000e+00s
    Theano Linker time (includes C, CUDA code generation/compiling): 9.608269e-05s