Tensorflow 如何让Keras LSTM在多变量环境中对多个时间序列进行预测？
我试图用Keras对多个变量同时进行预测。使用，我想预测所有特征的值，包括pm 2.5、DEWP、温度等，而不仅仅是污染（pm 2.5）。本质上，这是在给定所有变量的情况下，建立一个模型，将所有变量作为时间序列进行预测，而不仅仅是预测一个变量 我使用重塑后的3D数据修改了原始示例代码，但出现了一个错误。代码如下：Tensorflow 如何让Keras LSTM在多变量环境中对多个时间序列进行预测？,tensorflow,keras,time-series,lstm,recurrent-neural-network,Tensorflow,Keras,Time Series,Lstm,Recurrent Neural Network,我试图用Keras对多个变量同时进行预测。使用，我想预测所有特征的值，包括pm 2.5、DEWP、温度等，而不仅仅是污染（pm 2.5）。本质上，这是在给定所有变量的情况下，建立一个模型，将所有变量作为时间序列进行预测，而不仅仅是预测一个变量 我使用重塑后的3D数据修改了原始示例代码，但出现了一个错误。代码如下： from sklearn.preprocessing import LabelEncoder, MinMaxScaler from tensorflow.keras.models i
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from math import sqrt
from numpy import concatenate
from matplotlib import pyplot
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from pandas import read_csv, DataFrame, concat
from datetime import datetime
# load data
def parse(x):
return datetime.strptime(x, '%Y %m %d %H')
dataset = read_csv('raw.csv', parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)
dataset.drop('No', axis=1, inplace=True)
# manually specify column names
dataset.columns = ['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']
dataset.index.name = 'date'
# mark all NA values with 0
dataset['pollution'].fillna(0, inplace=True)
# drop the first 24 hours
dataset = dataset[24:]
# summarize first 5 rows
print(dataset.head(5))
# save to file
dataset.to_csv('pollution.csv')
# convert series to supervised learning
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
n_vars = 1 if type(data) is list else data.shape[1]
df = DataFrame(data)
cols, names = list(), list()
# input sequence (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
# forecast sequence (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i))
if i == 0:
names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
else:
names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
# put it all together
agg = concat(cols, axis=1)
agg.columns = names
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg
# load dataset
dataset = read_csv('pollution.csv', header=0, index_col=0)
values = dataset.values
# integer encode direction
encoder = LabelEncoder()
values[:,4] = encoder.fit_transform(values[:,4])
# ensure all data is float
values = values.astype('float32')
# normalize features
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
# frame as supervised learning
reframed = series_to_supervised(scaled, 1, 1)
# split into train and test sets
values = reframed.values
n_train_hours = 365 * 24
train = values[:n_train_hours, :]
test = values[n_train_hours:, :]
# split into input and outputs
train_X, train_y = train[:, :-8], train[:, -8:]
test_X, test_y = test[:, :-8], test[:, -8:]
# reshape input to be 3D [samples, timesteps, features]
train_X_3d = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X_3d = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
train_y_3d = train_y.reshape((train_y.shape[0], 1, train_y.shape[1]))
test_y_3d = test_y.reshape((test_y.shape[0], 1, test_y.shape[1]))
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
print(train_X_3d.shape, train_y_3d.shape, test_X_3d.shape, test_y_3d.shape)
# design network
model = Sequential()
model.add(LSTM(50, input_shape=(train_X_3d.shape[1], train_X_3d.shape[2])))
#model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
# fit network
history = model.fit(train_X_3d, train_y_3d,
epochs=50, batch_size=72,
validation_data=(test_X_3d, test_y_3d), verbose=2, shuffle=False)
# plot history
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='test')
pyplot.legend()
pyplot.show()
# make a prediction
yhat = model.predict(test_X_3d)
yhat
pollution dew temp press wnd_dir wnd_spd snow rain
date
2010-01-02 00:00:00 129.0 -16 -4.0 1020.0 SE 1.79 0 0
2010-01-02 01:00:00 148.0 -15 -4.0 1020.0 SE 2.68 0 0
2010-01-02 02:00:00 159.0 -11 -5.0 1021.0 SE 3.57 0 0
2010-01-02 03:00:00 181.0 -7 -5.0 1022.0 SE 5.36 1 0
2010-01-02 04:00:00 138.0 -7 -5.0 1022.0 SE 6.25 2 0
(8760, 8) (8760, 8) (35039, 8) (35039, 8)
(8760, 1, 8) (8760, 1, 8) (35039, 1, 8) (35039, 1, 8)
Train on 8760 samples, validate on 35039 samples
Epoch 1/50
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1609 try:
-> 1610 c_op = c_api.TF_FinishOperation(op_desc)
1611 except errors.InvalidArgumentError as e:
InvalidArgumentError: Dimensions must be equal, but are 50 and 8 for 'loss/lstm_loss/sub' (op: 'Sub') with input shapes: [?,50], [?,1,8].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-1-00c079ab5854> in <module>
97 history = model.fit(train_X_3d, train_y_3d,
98 epochs=50, batch_size=72,
---> 99 validation_data=(test_X_3d, test_y_3d), verbose=2, shuffle=False)
100 # plot history
101 pyplot.plot(history.history['loss'], label='train')
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/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, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
726 max_queue_size=max_queue_size,
727 workers=workers,
--> 728 use_multiprocessing=use_multiprocessing)
729
730 def evaluate(self,
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
322 mode=ModeKeys.TRAIN,
323 training_context=training_context,
--> 324 total_epochs=epochs)
325 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
326
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
121 step=step, mode=mode, size=current_batch_size) as batch_logs:
122 try:
--> 123 batch_outs = execution_function(iterator)
124 except (StopIteration, errors.OutOfRangeError):
125 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in execution_function(input_fn)
84 # `numpy` translates Tensors to values in Eager mode.
85 return nest.map_structure(_non_none_constant_value,
---> 86 distributed_function(input_fn))
87
88 return execution_function
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
501 # This is the first call of __call__, so we have to initialize.
502 initializer_map = object_identity.ObjectIdentityDictionary()
--> 503 self._initialize(args, kwds, add_initializers_to=initializer_map)
504 finally:
505 # At this point we know that the initialization is complete (or less
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
406 self._concrete_stateful_fn = (
407 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 408 *args, **kwds))
409
410 def invalid_creator_scope(*unused_args, **unused_kwds):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1846 if self.input_signature:
1847 args, kwargs = None, None
-> 1848 graph_function, _, _ = self._maybe_define_function(args, kwargs)
1849 return graph_function
1850
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2148 graph_function = self._function_cache.primary.get(cache_key, None)
2149 if graph_function is None:
-> 2150 graph_function = self._create_graph_function(args, kwargs)
2151 self._function_cache.primary[cache_key] = graph_function
2152 return graph_function, args, kwargs
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2039 arg_names=arg_names,
2040 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2041 capture_by_value=self._capture_by_value),
2042 self._function_attributes,
2043 # Tell the ConcreteFunction to clean up its graph once it goes out of
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
913 converted_func)
914
--> 915 func_outputs = python_func(*func_args, **func_kwargs)
916
917 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
356 # __wrapped__ allows AutoGraph to swap in a converted function. We give
357 # the function a weak reference to itself to avoid a reference cycle.
--> 358 return weak_wrapped_fn().__wrapped__(*args, **kwds)
359 weak_wrapped_fn = weakref.ref(wrapped_fn)
360
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in distributed_function(input_iterator)
71 strategy = distribution_strategy_context.get_strategy()
72 outputs = strategy.experimental_run_v2(
---> 73 per_replica_function, args=(model, x, y, sample_weights))
74 # Out of PerReplica outputs reduce or pick values to return.
75 all_outputs = dist_utils.unwrap_output_dict(
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in experimental_run_v2(self, fn, args, kwargs)
758 fn = autograph.tf_convert(fn, ag_ctx.control_status_ctx(),
759 convert_by_default=False)
--> 760 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
761
762 def reduce(self, reduce_op, value, axis):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in call_for_each_replica(self, fn, args, kwargs)
1785 kwargs = {}
1786 with self._container_strategy().scope():
-> 1787 return self._call_for_each_replica(fn, args, kwargs)
1788
1789 def _call_for_each_replica(self, fn, args, kwargs):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs)
2130 self._container_strategy(),
2131 replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):
-> 2132 return fn(*args, **kwargs)
2133
2134 def _reduce_to(self, reduce_op, value, destinations):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/autograph/impl/api.py in wrapper(*args, **kwargs)
290 def wrapper(*args, **kwargs):
291 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292 return func(*args, **kwargs)
293
294 if inspect.isfunction(func) or inspect.ismethod(func):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in train_on_batch(model, x, y, sample_weight, class_weight, reset_metrics)
262 y,
263 sample_weights=sample_weights,
--> 264 output_loss_metrics=model._output_loss_metrics)
265
266 if reset_metrics:
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py in train_on_batch(model, inputs, targets, sample_weights, output_loss_metrics)
309 sample_weights=sample_weights,
310 training=True,
--> 311 output_loss_metrics=output_loss_metrics))
312 if not isinstance(outs, list):
313 outs = [outs]
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py in _process_single_batch(model, inputs, targets, output_loss_metrics, sample_weights, training)
250 output_loss_metrics=output_loss_metrics,
251 sample_weights=sample_weights,
--> 252 training=training))
253 if total_loss is None:
254 raise ValueError('The model cannot be run '
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py in _model_loss(model, inputs, targets, output_loss_metrics, sample_weights, training)
164
165 if hasattr(loss_fn, 'reduction'):
--> 166 per_sample_losses = loss_fn.call(targets[i], outs[i])
167 weighted_losses = losses_utils.compute_weighted_loss(
168 per_sample_losses,
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/losses.py in call(self, y_true, y_pred)
219 y_pred, y_true = tf_losses_util.squeeze_or_expand_dimensions(
220 y_pred, y_true)
--> 221 return self.fn(y_true, y_pred, **self._fn_kwargs)
222
223 def get_config(self):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/keras/losses.py in mean_absolute_error(y_true, y_pred)
781 y_pred = ops.convert_to_tensor(y_pred)
782 y_true = math_ops.cast(y_true, y_pred.dtype)
--> 783 return K.mean(math_ops.abs(y_pred - y_true), axis=-1)
784
785
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/ops/math_ops.py in binary_op_wrapper(x, y)
897 with ops.name_scope(None, op_name, [x, y]) as name:
898 if isinstance(x, ops.Tensor) and isinstance(y, ops.Tensor):
--> 899 return func(x, y, name=name)
900 elif not isinstance(y, sparse_tensor.SparseTensor):
901 try:
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_math_ops.py in sub(x, y, name)
11086 # Add nodes to the TensorFlow graph.
11087 _, _, _op = _op_def_lib._apply_op_helper(
> 11088 "Sub", x=x, y=y, name=name)
11089 _result = _op.outputs[:]
11090 _inputs_flat = _op.inputs
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
791 op = g.create_op(op_type_name, inputs, dtypes=None, name=scope,
792 input_types=input_types, attrs=attr_protos,
--> 793 op_def=op_def)
794 return output_structure, op_def.is_stateful, op
795
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py in create_op(***failed resolving arguments***)
546 return super(FuncGraph, self)._create_op_internal( # pylint: disable=protected-access
547 op_type, inputs, dtypes, input_types, name, attrs, op_def,
--> 548 compute_device)
549
550 def capture(self, tensor, name=None):
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
3427 input_types=input_types,
3428 original_op=self._default_original_op,
-> 3429 op_def=op_def)
3430 self._create_op_helper(ret, compute_device=compute_device)
3431 return ret
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1771 op_def, inputs, node_def.attr)
1772 self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
-> 1773 control_input_ops)
1774 # pylint: enable=protected-access
1775
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1611 except errors.InvalidArgumentError as e:
1612 # Convert to ValueError for backwards compatibility.
-> 1613 raise ValueError(str(e))
1614
1615 return c_op
ValueError: Dimensions must be equal, but are 50 and 8 for 'loss/lstm_loss/sub' (op: 'Sub') with input shapes: [?,50], [?,1,8].
污染露水温度压力wnd_dir wnd_spd雪雨
日期
2010-01-02 00:00:00 129.0-16-4.0 1020.0东南1.79
2010-01-02 01:00:00 148.0-15-4.0 1020.0东南2.68
2010-01-02 02:00:00159.0-11-5.01021.0SE3.5700
2010-01-02 03:00:00 181.0-7-5.0 1022.0东南5.36 110
2010-01-02 04:00:00 138.0-7-5.0 1022.0东南6.25 2 0
(8760, 8) (8760, 8) (35039, 8) (35039, 8)
(8760, 1, 8) (8760, 1, 8) (35039, 1, 8) (35039, 1, 8)
培训8760个样本，验证35039个样本
纪元1/50
---------------------------------------------------------------------------
InvalidArgumentError回溯（最后一次最近调用）
~/anaconda3/envs/topic\u forecaster/lib/python3.7/site-packages/tensorflow\u core/python/framework/ops.py in\u create\u c\u op（图形、节点定义、输入、控制输入）
1609试试看：
->1610 c_op=c_api.TF_FinishOperation（op_desc）
1611错误除外。InvalidArgumentError为e:
InvalidArgumentError：尺寸必须相等，但对于输入形状为：[？、50]、，？、1,8]的“损失/lstm_损失/sub”（op:'sub'），尺寸分别为50和8。
在处理上述异常期间，发生了另一个异常：
ValueError回溯（最近一次调用上次）
在里面
97历史=模型拟合（列车X\U 3d、列车y\U 3d、，
98个时代=50，批次大小=72，
--->99验证数据=（test\ux\u3d，test\uy\u3d），verbose=2，shuffle=False）
100#绘图历史
101 pyplot.plot（history.history['loss']，label='train'）
~/anaconda3/envs/topic\u forecaster/lib/python3.7/site-packages/tensorflow\u core/python/keras/engine/training.py in-fit（self、x、y、批处理大小、历元、冗余、回调、验证拆分、验证数据、随机、类权重、样本权重、初始历元、每个历元的步骤、验证步骤、验证频率、最大队列大小、工人、使用多处理、**kwargs）
726最大队列大小=最大队列大小，
727名工人=工人，
-->728使用多处理=使用多处理）
729
730 def评估（自我，
~/anaconda3/envs/topic\u forecaster/lib/python3.7/site-packages/tensorflow\u core/python/keras/engine/training\u v2.py适合（self、model、x、y、批量大小、历元、冗余、回调、验证拆分、验证数据、无序、类权重、样本权重、初始历元、每历元的步骤、验证步骤、验证频率、**kwargs）
322模式=ModeKeys.TRAIN，
323培训上下文=培训上下文，
-->324个总记录（每个记录=个记录）
325 cbks.生成日志（模型、历元日志、训练结果、模式键.训练）
326
~/anaconda3/envs/topic\u forecaster/lib/python3.7/site-packages/tensorflow\u core/python/keras/engine/training\u v2.py in run\u one\u epoch（模型、迭代器、执行函数、数据集大小、批量大小、策略、每个epoch的步骤、样本数、模式、训练上下文、总epoch）
121步骤=步骤，模式=模式，大小=当前批次大小）作为批次日志：
请尝试：
-->123批处理输出=执行函数（迭代器）
124除外（StopIteration，errors.OutOfRangeError）：
125#TODO（kaftan）：关于tf函数和错误的文件错误。OutOfRangeError？
~/anaconda3/envs/topic\u forecaster/lib/python3.7/site-packages/tensorflow\u core/python/keras/engine/training\u v2\u utils.py in execution\u函数（输入\u fn）
84#`numpy`以渴望模式将张量转换为值。
85返回nest.map\u结构（\u非\u无\u常量\u值，
--->86分布函数（输入函数）
87
88返回执行函数
~/anaconda3/envs/topic\u forecaster/lib/python3.7/site-packages/tensorflow\u core/python/eager/def\u function.py in\uuuuu调用（self，*args，**kwds）
455
456跟踪\u计数=自。\u获取\u跟踪\u计数（）
-->457结果=自调用（*args，**kwds）
458如果跟踪计数==self.\u获取跟踪计数（）：
459 self.\u调用\u计数器。调用\u而不跟踪（）
~/anaconda3/envs/topic\u forecaster/lib/python3.7/site-packages/tensorflow\u core/python/eager/def\u function.py在调用中（self，*args，**kwds）
501#这是u call u的第一个调用，因此我们必须初始化。
502初始值设定项\u map=object\u identity.ObjectIdentityDictionary（）
-->503自我初始化（参数、KWD、添加初始值设定项到=初始值设定项映射）
504最后：
505#此时我们知道初始化已完成（或更少
~/anaconda3/envs/topic_forecaster/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in_initialize（self、args、kwds、add_initializer_to）
406自具体状态=(
407 self._stateful_fn._get_concrete_function_internal_garbage_collected（#pylint:disable=protected access
-->408*args，**科威特第纳尔）
409
410 def无效的创建者范围（*未使用的参数，**未使用的参数）：
~/anaconda3/envs/topic\u forecaster/lib/python3.7/site-packages/tensorflow\u core/python/eager/function.py in\u get\u concrete\u function\u internal\u garbage\u collected（self，*args，**kwargs）
1846如果self.input\u签名：
1847 args，kwargs=None，None
->1848图形函数，u，u=self._可能定义函数（args，kwargs）
1849年回归
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from math import sqrt
from numpy import array, concatenate
from matplotlib import pyplot
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from pandas import read_csv, DataFrame, concat
from datetime import datetime
# load data
def parse(x):
return datetime.strptime(x, '%Y %m %d %H')
dataset = read_csv('raw.csv', parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)
dataset.drop('No', axis=1, inplace=True)
# manually specify column names
dataset.columns = ['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']
dataset.index.name = 'date'
# mark all NA values with 0
dataset['pollution'].fillna(0, inplace=True)
# drop the first 24 hours
dataset = dataset[24:]
# summarize first 5 rows
print(dataset.head(5))
# save to file
dataset.to_csv('pollution.csv')
# load dataset
dataset = read_csv('pollution.csv', header=0, index_col=0)
values = dataset.values
# integer encode direction
encoder = LabelEncoder()
values[:,4] = encoder.fit_transform(values[:,4])
# ensure all data is float
values = values.astype('float32')
# normalize features
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
n_steps = 10
n_features = 8
def split_sequences(sequences, n_steps):
X, y = list(), list()
for i in range(len(sequences)):
# find the end of this pattern
end_ix = i + n_steps
# check if we are beyond the dataset
if end_ix > len(sequences)-1:
break
# gather input and output parts of the pattern
seq_x, seq_y = sequences[i:end_ix, :], sequences[end_ix, :]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
X, y = split_sequences(sequences=scaled, n_steps=n_steps)
print(X.shape, y.shape)
X = X[:1000, :]
y = y[:1000, :]
# define model
model = Sequential()
model.add(LSTM(50, activation='relu', return_sequences=False, input_shape=(n_steps, n_features)))
#model.add(LSTM(100, activation='relu'))
model.add(Dense(n_features))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=100, verbose=1)
# demonstrate prediction
x_input = X[0]
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)