TypeError: Unknown parameter type: <class 'theano.tensor.var.TensorVariable'>

[eleniboukouvala]

I tried to run the example notebook and at first it worked fine, but when I run it after some time, I got this error TypeError: Unknown parameter type: <class 'theano.tensor.var.TensorVariable'>. I didn't change anything in the given code.

/usr/local/lib/python3.7/dist-packages/deepsurv/deep_surv.py in train(self, train_data, valid_data, n_epochs, validation_frequency, patience, improvement_threshold, patience_increase, logger, update_fn, verbose, **kwargs)
414 learning_rate=lr,
415 momentum = momentum,
--> 416 update_fn = update_fn, **kwargs
417 )
418

/usr/local/lib/python3.7/dist-packages/deepsurv/deep_surv.py in _get_train_valid_fn(self, L1_reg, L2_reg, learning_rate, **kwargs)
252 loss, updates = self._get_loss_updates(
253 L1_reg, L2_reg, deterministic = False,
--> 254 learning_rate=learning_rate, **kwargs
255 )
256 train_fn = theano.function(

/usr/local/lib/python3.7/dist-packages/deepsurv/deep_surv.py in _get_loss_updates(self, L1_reg, L2_reg, update_fn, max_norm, deterministic, momentum, **kwargs)
199 self._negative_log_likelihood(self.E, deterministic)
200 + regularize_layer_params(self.network,l1) * L1_reg
--> 201 + regularize_layer_params(self.network, l2) * L2_reg
202 )
203

/usr/local/lib/python3.7/dist-packages/deepsurv/deep_surv.py in _negative_log_likelihood(self, E, deterministic)
160 partial Cox likelihood
161 """
--> 162 risk = self.risk(deterministic)
163 hazard_ratio = T.exp(risk)
164 log_risk = T.log(T.extra_ops.cumsum(hazard_ratio))

/usr/local/lib/python3.7/dist-packages/deepsurv/deep_surv.py in risk(self, deterministic)
561 """
562 return lasagne.layers.get_output(self.network, deterministic = deterministic)
--> 563
564 def predict_risk(self, x):
565 """

/usr/local/lib/python3.7/dist-packages/lasagne/layers/helper.py in get_output(layer_or_layers, inputs, **kwargs)
195 "mapping this layer to an input expression."
196 % layer)
--> 197 all_outputs[layer] = layer.get_output_for(layer_inputs, **kwargs)
198 try:
199 accepted_kwargs |= set(utils.inspect_kwargs(

/usr/local/lib/python3.7/dist-packages/lasagne/layers/noise.py in get_output_for(self, input, deterministic, **kwargs)
100 for a, s in enumerate(mask_shape))
101 mask = self._srng.binomial(mask_shape, p=retain_prob, dtype=input.dtype)
--> 102 if self.shared_axes:
103 bcast = tuple(bool(s == 1) for s in mask_shape)
104 mask = T.patternbroadcast(mask, bcast)

/usr/local/lib/python3.7/dist-packages/theano/sandbox/rng_mrg.py in binomial(self, size, n, p, ndim, dtype, nstreams, **kwargs)
899 p = undefined_grad(as_tensor_variable(p))
900 x = self.uniform(size=size, nstreams=nstreams, **kwargs)
--> 901 return cast(x < p, dtype)
902 else:
903 raise NotImplementedError("MRG_RandomStreams.binomial with n > 1")

/usr/local/lib/python3.7/dist-packages/theano/sandbox/rng_mrg.py in uniform(self, size, low, high, ndim, dtype, nstreams, **kwargs)
870 nstreams = self.n_streams(size)
871 rstates = self.get_substream_rstates(nstreams, dtype)
--> 872
873 d = {}
874 if 'target' in kwargs:

/usr/local/lib/python3.7/dist-packages/theano/configparser.py in res(*args, **kwargs)
115 def res(*args, **kwargs):
116 with self:
--> 117 return f(*args, **kwargs)
118 return res
119

/usr/local/lib/python3.7/dist-packages/theano/sandbox/rng_mrg.py in get_substream_rstates(self, n_streams, dtype, inc_rstate)
777 if multMatVect.dot_modulo is None:
778 multMatVect(rval[0], A1p72, M1, A2p72, M2)
--> 779
780 # This way of calling the Theano fct is done to bypass Theano overhead.
781 f = multMatVect.dot_modulo

/usr/local/lib/python3.7/dist-packages/theano/sandbox/rng_mrg.py in multMatVect(v, A, m1, B, m2)
60 o = DotModulo()(A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym)
61 multMatVect.dot_modulo = function([A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym], o, profile=False)
---> 62
63 # This way of calling the Theano fct is done to bypass Theano overhead.
64 f = multMatVect.dot_modulo

/usr/local/lib/python3.7/dist-packages/theano/compile/function/init.py in function(inputs, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input)
348 on_unused_input=on_unused_input,
349 profile=profile,
--> 350 output_keys=output_keys,
351 )
352 return fn

/usr/local/lib/python3.7/dist-packages/theano/compile/function/pfunc.py in pfunc(params, outputs, mode, updates, givens, no_default_updates, accept_inplace, name, rebuild_strict, allow_input_downcast, profile, on_unused_input, output_keys)
425 # transform params into theano.compile.In objects.
426 inputs = [
--> 427 _pfunc_param_to_in(p, allow_downcast=allow_input_downcast) for p in params
428 ]
429

/usr/local/lib/python3.7/dist-packages/theano/compile/function/pfunc.py in (.0)
425 # transform params into theano.compile.In objects.
426 inputs = [
--> 427 _pfunc_param_to_in(p, allow_downcast=allow_input_downcast) for p in params
428 ]
429

/usr/local/lib/python3.7/dist-packages/theano/compile/function/pfunc.py in _pfunc_param_to_in(param, strict, allow_downcast)
541 elif isinstance(param, In):
542 return param
--> 543 raise TypeError(f"Unknown parameter type: {type(param)}")
544
545

TypeError: Unknown parameter type: <class 'theano.tensor.var.TensorVariable'>