DeepIV Value Overflow about econml HOT 9 OPEN

WARNING:tensorflow:From /Users/jinhuawang/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Train on 400 samples, validate on 45 samples
Epoch 1/30
400/400 [==============================] - 1s 1ms/step - loss: nan - val_loss: nan
Epoch 2/30
400/400 [==============================] - 0s 57us/step - loss: nan - val_loss: nan
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-6-9c6d6ed20ebe> in <module>
      1 boot_est = BootstrapEstimator(deepIvEst, n_bootstrap_samples=2, n_jobs=1)
----> 2 boot_est.fit(Y=y,T=t,X=x,Z=z)

~/Dropbox (Cambridge University)/EconML/econml/bootstrap.py in fit(self, *args, **named_args)
     56         Parallel(n_jobs=self._n_jobs, prefer='threads', verbose=3)(
     57             (obj.fit, [arg[inds] for arg in args], {arg: named_args[arg][inds] for arg in named_args})
---> 58             for obj, inds in zip(self._instances, indices)
     59         )
     60         return self

~/anaconda3/lib/python3.6/site-packages/joblib-0.13.2-py3.6.egg/joblib/parallel.py in __call__(self, iterable)
    919             # remaining jobs.
    920             self._iterating = False
--> 921             if self.dispatch_one_batch(iterator):
    922                 self._iterating = self._original_iterator is not None
    923 

~/anaconda3/lib/python3.6/site-packages/joblib-0.13.2-py3.6.egg/joblib/parallel.py in dispatch_one_batch(self, iterator)
    757                 return False
    758             else:
--> 759                 self._dispatch(tasks)
    760                 return True
    761 

~/anaconda3/lib/python3.6/site-packages/joblib-0.13.2-py3.6.egg/joblib/parallel.py in _dispatch(self, batch)
    714         with self._lock:
    715             job_idx = len(self._jobs)
--> 716             job = self._backend.apply_async(batch, callback=cb)
    717             # A job can complete so quickly than its callback is
    718             # called before we get here, causing self._jobs to

~/anaconda3/lib/python3.6/site-packages/joblib-0.13.2-py3.6.egg/joblib/_parallel_backends.py in apply_async(self, func, callback)
    180     def apply_async(self, func, callback=None):
    181         """Schedule a func to be run"""
--> 182         result = ImmediateResult(func)
    183         if callback:
    184             callback(result)

~/anaconda3/lib/python3.6/site-packages/joblib-0.13.2-py3.6.egg/joblib/_parallel_backends.py in __init__(self, batch)
    547         # Don't delay the application, to avoid keeping the input
    548         # arguments in memory
--> 549         self.results = batch()
    550 
    551     def get(self):

~/anaconda3/lib/python3.6/site-packages/joblib-0.13.2-py3.6.egg/joblib/parallel.py in __call__(self)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

~/anaconda3/lib/python3.6/site-packages/joblib-0.13.2-py3.6.egg/joblib/parallel.py in <listcomp>(.0)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

~/Dropbox (Cambridge University)/EconML/econml/deepiv.py in fit(self, Y, T, X, Z)
    329         model.compile(self._optimizer)
    330         # TODO: do we need to give the user more control over other arguments to fit?
--> 331         model.fit([Z, X, T], [], **self._first_stage_options)
    332 
    333         lm = response_loss_model(lambda t, x: self._h(t, x),

~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
   1037                                         initial_epoch=initial_epoch,
   1038                                         steps_per_epoch=steps_per_epoch,
-> 1039                                         validation_steps=validation_steps)
   1040 
   1041     def evaluate(self, x=None, y=None,

~/anaconda3/lib/python3.6/site-packages/keras/engine/training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
    215                         for l, o in zip(out_labels, val_outs):
    216                             epoch_logs['val_' + l] = o
--> 217         callbacks.on_epoch_end(epoch, epoch_logs)
    218         if callback_model.stop_training:
    219             break

~/anaconda3/lib/python3.6/site-packages/keras/callbacks.py in on_epoch_end(self, epoch, logs)
     77         logs = logs or {}
     78         for callback in self.callbacks:
---> 79             callback.on_epoch_end(epoch, logs)
     80 
     81     def on_batch_begin(self, batch, logs=None):

~/anaconda3/lib/python3.6/site-packages/keras/callbacks.py in on_epoch_end(self, epoch, logs)
    555                         print('Restoring model weights from the end of '
    556                               'the best epoch')
--> 557                     self.model.set_weights(self.best_weights)
    558 
    559     def on_train_end(self, logs=None):

~/anaconda3/lib/python3.6/site-packages/keras/engine/network.py in set_weights(self, weights)
    502         for layer in self.layers:
    503             num_param = len(layer.weights)
--> 504             layer_weights = weights[:num_param]
    505             for sw, w in zip(layer.weights, layer_weights):
    506                 tuples.append((sw, w))

TypeError: 'NoneType' object is not subscriptable

x = np.random.uniform(low=0.0, high=1000.0, size=(445,1))
x=x/100
z = np.random.uniform(low=0.0, high=1000.0, size=(445,1))
z=z/100