Mcfly generate models gives NotImplementedError #15
Description
I tried to run notebooks/TS_dataset_generation_benchmarking1.ipynb notebook However when calling cell
num_classes = y_train_binary.shape[1]
models = modelgen.generate_models(np.swapaxes(X_train,1,2).shape,
number_of_classes=num_classes,
number_of_models = 12)I got an `NotImplementedError: Cannot convert a symbolic Tensor (lstm/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported`
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-20-2407be9b3991> in <module>
1 num_classes = y_train_binary.shape[1]
2
----> 3 models = modelgen.generate_models(np.swapaxes(X_train,1,2).shape,
4 number_of_classes=num_classes,
5 number_of_models = 12)
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/mcfly/modelgen.py in generate_models(x_shape, number_of_classes, number_of_models, model_types, metrics, **hyperparameter_ranges)
116
117 hyperparameters = model_type.generate_hyperparameters()
--> 118 model = model_type.create_model(**hyperparameters)
119 model_name = model_type.model_name
120
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/mcfly/models/deep_conv_lstm.py in create_model(self, filters, lstm_dims, learning_rate, regularization_rate)
147
148 for lstm_dim in lstm_dims:
--> 149 model.add(LSTM(units=lstm_dim, return_sequences=True,
150 activation='tanh'))
151
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
515 self._self_setattr_tracking = False # pylint: disable=protected-access
516 try:
--> 517 result = method(self, *args, **kwargs)
518 finally:
519 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py in add(self, layer)
221 # If the model is being built continuously on top of an input layer:
222 # refresh its output.
--> 223 output_tensor = layer(self.outputs[0])
224 if len(nest.flatten(output_tensor)) != 1:
225 raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
658
659 if initial_state is None and constants is None:
--> 660 return super(RNN, self).__call__(inputs, **kwargs)
661
662 # If any of `initial_state` or `constants` are specified and are Keras
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self, *args, **kwargs)
949 # >> model = tf.keras.Model(inputs, outputs)
950 if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
--> 951 return self._functional_construction_call(inputs, args, kwargs,
952 input_list)
953
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
1088 layer=self, inputs=inputs, build_graph=True, training=training_value):
1089 # Check input assumptions set after layer building, e.g. input shape.
-> 1090 outputs = self._keras_tensor_symbolic_call(
1091 inputs, input_masks, args, kwargs)
1092
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
820 return nest.map_structure(keras_tensor.KerasTensor, output_signature)
821 else:
--> 822 return self._infer_output_signature(inputs, args, kwargs, input_masks)
823
824 def _infer_output_signature(self, inputs, args, kwargs, input_masks):
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks)
861 # TODO(kaftan): do we maybe_build here, or have we already done it?
862 self._maybe_build(inputs)
--> 863 outputs = call_fn(inputs, *args, **kwargs)
864
865 self._handle_activity_regularization(inputs, outputs)
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent_v2.py in call(self, inputs, mask, training, initial_state)
1155
1156 # LSTM does not support constants. Ignore it during process.
-> 1157 inputs, initial_state, _ = self._process_inputs(inputs, initial_state, None)
1158
1159 if isinstance(mask, list):
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py in _process_inputs(self, inputs, initial_state, constants)
857 initial_state = self.states
858 elif initial_state is None:
--> 859 initial_state = self.get_initial_state(inputs)
860
861 if len(initial_state) != len(self.states):
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py in get_initial_state(self, inputs)
640 dtype = inputs.dtype
641 if get_initial_state_fn:
--> 642 init_state = get_initial_state_fn(
643 inputs=None, batch_size=batch_size, dtype=dtype)
644 else:
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py in get_initial_state(self, inputs, batch_size, dtype)
2504
2505 def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
-> 2506 return list(_generate_zero_filled_state_for_cell(
2507 self, inputs, batch_size, dtype))
2508
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py in _generate_zero_filled_state_for_cell(cell, inputs, batch_size, dtype)
2985 batch_size = array_ops.shape(inputs)[0]
2986 dtype = inputs.dtype
-> 2987 return _generate_zero_filled_state(batch_size, cell.state_size, dtype)
2988
2989
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py in _generate_zero_filled_state(batch_size_tensor, state_size, dtype)
3001
3002 if nest.is_nested(state_size):
-> 3003 return nest.map_structure(create_zeros, state_size)
3004 else:
3005 return create_zeros(state_size)
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/util/nest.py in map_structure(func, *structure, **kwargs)
657
658 return pack_sequence_as(
--> 659 structure[0], [func(*x) for x in entries],
660 expand_composites=expand_composites)
661
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/util/nest.py in <listcomp>(.0)
657
658 return pack_sequence_as(
--> 659 structure[0], [func(*x) for x in entries],
660 expand_composites=expand_composites)
661
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py in create_zeros(unnested_state_size)
2998 flat_dims = tensor_shape.TensorShape(unnested_state_size).as_list()
2999 init_state_size = [batch_size_tensor] + flat_dims
-> 3000 return array_ops.zeros(init_state_size, dtype=dtype)
3001
3002 if nest.is_nested(state_size):
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
199 """Call target, and fall back on dispatchers if there is a TypeError."""
200 try:
--> 201 return target(*args, **kwargs)
202 except (TypeError, ValueError):
203 # Note: convert_to_eager_tensor currently raises a ValueError, not a
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py in wrapped(*args, **kwargs)
2817
2818 def wrapped(*args, **kwargs):
-> 2819 tensor = fun(*args, **kwargs)
2820 tensor._is_zeros_tensor = True
2821 return tensor
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py in zeros(shape, dtype, name)
2866 # Create a constant if it won't be very big. Otherwise create a fill
2867 # op to prevent serialized GraphDefs from becoming too large.
-> 2868 output = _constant_if_small(zero, shape, dtype, name)
2869 if output is not None:
2870 return output
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py in _constant_if_small(value, shape, dtype, name)
2802 def _constant_if_small(value, shape, dtype, name):
2803 try:
-> 2804 if np.prod(shape) < 1000:
2805 return constant(value, shape=shape, dtype=dtype, name=name)
2806 except TypeError:
<__array_function__ internals> in prod(*args, **kwargs)
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py in prod(a, axis, dtype, out, keepdims, initial, where)
3028 10
3029 """
-> 3030 return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,
3031 keepdims=keepdims, initial=initial, where=where)
3032
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/numpy/core/fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
85 return reduction(axis=axis, out=out, **passkwargs)
86
---> 87 return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
88
89
~/git/epodium/time_series_generator/env/lib/python3.8/site-packages/tensorflow/python/framework/ops.py in __array__(self)
850
851 def __array__(self):
--> 852 raise NotImplementedError(
853 "Cannot convert a symbolic Tensor ({}) to a numpy array."
854 " This error may indicate that you're trying to pass a Tensor to"
NotImplementedError: Cannot convert a symbolic Tensor (lstm/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported
I installed mcfly==3.1.0 with pip.