问题描述
继续上一个问题:Tensorflow - TypeError: 'int' 对象不可迭代
我的训练数据是一个列表,每个列表包含 1000 个浮点数.例如,x_train[0] =
My training data is a list of lists each comprised of 1000 floats. For example, x_train[0] =
[0.0, 0.0, 0.1, 0.25, 0.5, ...]
这是我的模型:
model = Sequential()
model.add(LSTM(128, activation='relu',
input_shape=(1000, 1), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
opt = tf.keras.optimizers.Adam(lr=1e-3, decay=1e-5)
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=3, validation_data=(x_test, y_test))
这是我得到的错误:
Traceback (most recent call last):
File "C:UsersencuDesktopProjectFilesCodeProgram.py", line 88, in FitModel
model.fit(x_train, y_train, epochs=3, validation_data=(x_test, y_test))
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonkerasengine raining.py", line 728, in fit
use_multiprocessing=use_multiprocessing)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonkerasengine raining_v2.py", line 224, in fit
distribution_strategy=strategy)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonkerasengine raining_v2.py", line 547, in _process_training_inputs
use_multiprocessing=use_multiprocessing)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonkerasengine raining_v2.py", line 606, in _process_inputs
use_multiprocessing=use_multiprocessing)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonkerasenginedata_adapter.py", line 479, in __init__
batch_size=batch_size, shuffle=shuffle, **kwargs)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonkerasenginedata_adapter.py", line 321, in __init__
dataset_ops.DatasetV2.from_tensors(inputs).repeat()
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythondataopsdataset_ops.py", line 414, in from_tensors
return TensorDataset(tensors)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythondataopsdataset_ops.py", line 2335, in __init__
element = structure.normalize_element(element)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythondatautilstructure.py", line 111, in normalize_element
ops.convert_to_tensor(t, name="component_%d" % i))
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonframeworkops.py", line 1184, in convert_to_tensor
return convert_to_tensor_v2(value, dtype, preferred_dtype, name)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonframeworkops.py", line 1242, in convert_to_tensor_v2
as_ref=False)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonframeworkops.py", line 1296, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonframework ensor_conversion_registry.py", line 52, in _default_conversion_function
return constant_op.constant(value, dtype, name=name)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonframeworkconstant_op.py", line 227, in constant
allow_broadcast=True)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonframeworkconstant_op.py", line 235, in _constant_impl
t = convert_to_eager_tensor(value, ctx, dtype)
File "C:UsersencuAppDataLocalProgramsPythonPython37libsite-packages ensorflow_corepythonframeworkconstant_op.py", line 96, in convert_to_eager_tensor
return ops.EagerTensor(value, ctx.device_name, dtype)
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float).
我自己尝试在谷歌上搜索错误,我发现了一些关于使用 tf.convert_to_tensor
函数的信息.我尝试通过这个传递我的训练和测试列表,但函数不会接受它们.
I've tried googling the error myself, I found something about using the tf.convert_to_tensor
function. I tried passing my training and testing lists through this but the function won't take them.
推荐答案
TL;DR 几个可能的错误,大部分用 x = np.asarray(x).astype('float32')
.
TL;DR Several possible errors, most fixed with x = np.asarray(x).astype('float32')
.
其他可能是数据预处理有问题;确保所有内容格式正确(分类、nans、字符串等).下面显示了模型的期望:
Others may be faulty data preprocessing; ensure everything is properly formatted (categoricals, nans, strings, etc). Below shows what the model expects:
[print(i.shape, i.dtype) for i in model.inputs]
[print(o.shape, o.dtype) for o in model.outputs]
[print(l.name, l.input_shape, l.dtype) for l in model.layers]
问题的根源在于使用 lists 作为输入,而不是 Numpy 数组;Keras/TF 不支持前者.一个简单的转换是:x_array = np.asarray(x_list)
.
The problem's rooted in using lists as inputs, as opposed to Numpy arrays; Keras/TF doesn't support former. A simple conversion is: x_array = np.asarray(x_list)
.
下一步是确保以预期格式提供数据;对于 LSTM,这将是一个尺寸为 (batch_size, timesteps, features)
的 3D 张量 - 或等效地,(num_samples, timesteps, channels)
.最后,作为调试专业提示,打印数据的所有形状.完成上述所有操作的代码如下:
The next step's to ensure data is fed in expected format; for LSTM, that'd be a 3D tensor with dimensions (batch_size, timesteps, features)
- or equivalently, (num_samples, timesteps, channels)
. Lastly, as a debug pro-tip, print ALL the shapes for your data. Code accomplishing all of the above, below:
Sequences = np.asarray(Sequences)
Targets = np.asarray(Targets)
show_shapes()
Sequences = np.expand_dims(Sequences, -1)
Targets = np.expand_dims(Targets, -1)
show_shapes()
# OUTPUTS
Expected: (num_samples, timesteps, channels)
Sequences: (200, 1000)
Targets: (200,)
Expected: (num_samples, timesteps, channels)
Sequences: (200, 1000, 1)
Targets: (200, 1)
作为额外提示,我注意到您通过 main()
运行,因此您的 IDE 可能缺少类似 Jupyter 的基于单元格的执行;我强烈推荐 Spyder IDE.就像在下面添加 # In[]
,然后按 Ctrl + Enter
一样简单:
As a bonus tip, I notice you're running via main()
, so your IDE probably lacks a Jupyter-like cell-based execution; I strongly recommend the Spyder IDE. It's as simple as adding # In[]
, and pressing Ctrl + Enter
below:
使用的函数:
def show_shapes(): # can make yours to take inputs; this'll use local variable values
print("Expected: (num_samples, timesteps, channels)")
print("Sequences: {}".format(Sequences.shape))
print("Targets: {}".format(Targets.shape))
这篇关于Tensorflow - ValueError:无法将 NumPy 数组转换为张量(不支持的对象类型浮点数)的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!