def main():
t0 = time.time()
# 选择模型
model = build_lstm_model()
# 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=['accuracy'])
# 训练模型
checkpoint = ModelCheckpoint('model_checkpoint.h5', save_weights_only=True, verbose=1, save_freq='epoch')
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, callbacks=[checkpoint])
# 评估模型
loss, accuracy = model.evaluate(x_test, y_test)
print(f"Test Loss: {loss}, Test Accuracy: {accuracy}")
t1 = time.time()
print(f"模型运行的时间为：{t1 - t0:.2f} 秒")
if __name__ == '__main__':
main()
10秒
WARNING:tensorflow:From /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/initializers.py:118: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:From /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1623: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
NotImplementedError: Cannot convert a symbolic Tensor (lstm/strided_slice:0) to a numpy array.
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
Cell In[21], line 24
21 print(f"模型运行的时间为：{t1 - t0:.2f} 秒")
23 if __name__ == '__main__':
---> 24 main()
Cell In[21], line 5, in main()
2 t0 = time.time()
4 # 选择模型
----> 5 model = build_lstm_model()
7 # 编译模型
8 model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
9 loss=tf.keras.losses.BinaryCrossentropy(),
10 metrics=['accuracy'])
Cell In[15], line 4, in build_lstm_model()
3 def build_lstm_model():
----> 4 model = keras.Sequential([
5 layers.Embedding(total_words, embedding_len, input_length=max_review_len),
6 layers.LSTM(64, return_sequences=False),
7 layers.Dense(1, activation='sigmoid')
8 ])
9 return model
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/training/tracking/base.py:457, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs)
455 self._self_setattr_tracking = False # pylint: disable=protected-access
456 try:
--> 457 result = method(self, *args, **kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/sequential.py:113, in Sequential.__init__(self, layers, name)
111 tf_utils.assert_no_legacy_layers(layers)
112 for layer in layers:
--> 113 self.add(layer)
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/training/tracking/base.py:457, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs)
455 self._self_setattr_tracking = False # pylint: disable=protected-access
456 try:
--> 457 result = method(self, *args, **kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/sequential.py:195, in Sequential.add(self, layer)
190 self.inputs = layer_utils.get_source_inputs(self.outputs[0])
192 elif self.outputs:
193 # If the model is being built continuously on top of an input layer:
194 # refresh its output.
--> 195 output_tensor = layer(self.outputs[0])
196 if len(nest.flatten(output_tensor)) != 1:
197 raise TypeError('All layers in a Sequential model '
198 'should have a single output tensor. '
199 'For multi-output layers, '
200 'use the functional API.')
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:623, in RNN.__call__(self, inputs, initial_state, constants, **kwargs)
617 inputs, initial_state, constants = _standardize_args(inputs,
618 initial_state,
619 constants,
620 self._num_constants)
622 if initial_state is None and constants is None:
--> 623 return super(RNN, self).__call__(inputs, **kwargs)
625 # If any of `initial_state` or `constants` are specified and are Keras
626 # tensors, then add them to the inputs and temporarily modify the
627 # input_spec to include them.
629 additional_inputs = []
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/base_layer.py:854, in Layer.__call__(self, inputs, *args, **kwargs)
852 outputs = base_layer_utils.mark_as_return(outputs, acd)
853 else:
--> 854 outputs = call_fn(cast_inputs, *args, **kwargs)
856 except errors.OperatorNotAllowedInGraphError as e:
857 raise TypeError('You are attempting to use Python control '
858 'flow in a layer that was not declared to be '
859 'dynamic. Pass `dynamic=True` to the class '
860 'constructor.\nEncountered error:\n"""\n' +
861 str(e) + '\n"""')
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2548, in LSTM.call(self, inputs, mask, training, initial_state)
2546 self.cell.reset_dropout_mask()
2547 self.cell.reset_recurrent_dropout_mask()
-> 2548 return super(LSTM, self).call(
2549 inputs, mask=mask, training=training, initial_state=initial_state)
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:681, in RNN.call(self, inputs, mask, training, initial_state, constants)
675 def call(self,
676 inputs,
677 mask=None,
678 training=None,
679 initial_state=None,
680 constants=None):
--> 681 inputs, initial_state, constants = self._process_inputs(
682 inputs, initial_state, constants)
684 if mask is not None:
685 # Time step masks must be the same for each input.
686 # TODO(scottzhu): Should we accept multiple different masks?
687 mask = nest.flatten(mask)[0]
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:798, in RNN._process_inputs(self, inputs, initial_state, constants)
796 initial_state = self.states
797 else:
--> 798 initial_state = self.get_initial_state(inputs)
800 if len(initial_state) != len(self.states):
801 raise ValueError('Layer has ' + str(len(self.states)) +
802 ' states but was passed ' + str(len(initial_state)) +
803 ' initial states.')
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:605, in RNN.get_initial_state(self, inputs)
603 dtype = inputs.dtype
604 if get_initial_state_fn:
--> 605 init_state = get_initial_state_fn(
606 inputs=None, batch_size=batch_size, dtype=dtype)
607 else:
608 init_state = _generate_zero_filled_state(batch_size, self.cell.state_size,
609 dtype)
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2313, in LSTMCell.get_initial_state(self, inputs, batch_size, dtype)
2312 def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
-> 2313 return list(_generate_zero_filled_state_for_cell(
2314 self, inputs, batch_size, dtype))
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2752, in _generate_zero_filled_state_for_cell(cell, inputs, batch_size, dtype)
2750 batch_size = array_ops.shape(inputs)[0]
2751 dtype = inputs.dtype
-> 2752 return _generate_zero_filled_state(batch_size, cell.state_size, dtype)
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2768, in _generate_zero_filled_state(batch_size_tensor, state_size, dtype)
2765 return array_ops.zeros(init_state_size, dtype=dtype)
2767 if nest.is_sequence(state_size):
-> 2768 return nest.map_structure(create_zeros, state_size)
2769 else:
2770 return create_zeros(state_size)
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/util/nest.py:536, in map_structure(func, *structure, **kwargs)
532 flat_structure = [flatten(s, expand_composites) for s in structure]
533 entries = zip(*flat_structure)
535 return pack_sequence_as(
--> 536 structure[0], [func(*x) for x in entries],
537 expand_composites=expand_composites)
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/util/nest.py:536, in <listcomp>(.0)
532 flat_structure = [flatten(s, expand_composites) for s in structure]
533 entries = zip(*flat_structure)
535 return pack_sequence_as(
--> 536 structure[0], [func(*x) for x in entries],
537 expand_composites=expand_composites)
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2765, in _generate_zero_filled_state.<locals>.create_zeros(unnested_state_size)
2763 flat_dims = tensor_shape.as_shape(unnested_state_size).as_list()
2764 init_state_size = [batch_size_tensor] + flat_dims
-> 2765 return array_ops.zeros(init_state_size, dtype=dtype)
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/ops/array_ops.py:2338, in zeros(shape, dtype, name)
2334 if not isinstance(shape, ops.Tensor):
2335 try:
2336 # Create a constant if it won't be very big. Otherwise create a fill op
2337 # to prevent serialized GraphDefs from becoming too large.
-> 2338 output = _constant_if_small(zero, shape, dtype, name)
2339 if output is not None:
2340 return output
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/ops/array_ops.py:2295, in _constant_if_small(value, shape, dtype, name)
2293 def _constant_if_small(value, shape, dtype, name):
2294 try:
-> 2295 if np.prod(shape) < 1000:
2296 return constant(value, shape=shape, dtype=dtype, name=name)
2297 except TypeError:
2298 # Happens when shape is a Tensor, list with Tensor elements, etc.
File <__array_function__ internals>:180, in prod(*args, **kwargs)
File /opt/conda/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3088, in prod(a, axis, dtype, out, keepdims, initial, where)
2970 @array_function_dispatch(_prod_dispatcher)
2971 def prod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,
2972 initial=np._NoValue, where=np._NoValue):
2973 """
2974 Return the product of array elements over a given axis.
2975
(...)
3086 10
3087 """
-> 3088 return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,
3089 keepdims=keepdims, initial=initial, where=where)
File /opt/conda/lib/python3.8/site-packages/numpy/core/fromnumeric.py:86, in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
83 else:
84 return reduction(axis=axis, out=out, **passkwargs)
---> 86 return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/framework/ops.py:735, in Tensor.__array__(self)
734 def __array__(self):
--> 735 raise NotImplementedError("Cannot convert a symbolic Tensor ({}) to a numpy"
736 " array.".format(self.name))
NotImplementedError: Cannot convert a symbolic Tensor (lstm/strided_slice:0) to a numpy array.
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