tensorflow keras model.fit上的图执行错误

Question

我真的很感激你从一开始就帮我。

我正在尝试用tensorflow.keras制作一个简单的NN

它以前工作过，但从昨天起，我的代码给出了“图形执行错误”

我想知道哪个优化器和激活函数对我的神经网络有好处，

所以我定义了一个函数来方便地进行迭代。

x_train是有56栏的熊猫DataFrame，y_train是有2栏的熊猫DataFrame。

我猜输入维没有问题。

你们能帮我解决这个错误吗？

import ccxt
import pandas as pd
import numpy as np
import tensorflow as tf

import time
import math

import csv

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(data_4candles, data_minmax)
x_test, x_val, y_test, y_val = train_test_split(x_test, y_test)

def model_compile_fit_predict(optimizer,activation,epochs,filepath):
    model = tf.keras.models.Sequential()

    model.add(tf.keras.layers.Dense(56, input_dim = 56, activation = activation))
    model.add(tf.keras.layers.Dense(56, activation = activation))
    model.add(tf.keras.layers.Dense(10, activation = activation))
    model.add(tf.keras.layers.Dense(2, activation = activation))

    
    model.compile(loss = 'mse', optimizer = optimizer, 
              metrics = ['accuracy',tf.keras.metrics.Precision(),tf.keras.metrics.Recall()])
    
    hist = model.fit(x_train, y_train, epochs = epochs, validation_data = (x_val, y_val))
    
    loss, accuracy, precision, recall_1 = model.evaluate(x_test, y_test)
    
    hist_acc = hist.history['accuracy']
    hist_loss = hist.history['loss']
    hist_val_acc = hist.history['val_accuracy']
    hist_val_loss = hist.history['val_loss']
    
    model.save(filepath = filepath)
    
    
    output = [activation, optimizer, 
              'test accuracy and loss: ', accuracy, loss, 
              'true positive / (true positive + false positive): ', precision,
              'true positive / (true positive + false negative): ', recall_1,
              'train epoch accuracy and loss: ',hist_acc,hist_loss,
              'validation epoch accuracy and loss: ', hist_val_acc,hist_val_loss]
    
    return output

activation_functions = ['relu', 'softplus', 'selu', 'elu']
optimizers = ['RMSprop', 'Adam', 'Adadelta', 
              'Adagrad', 'Adamax', 'Nadam', 'Ftrl']


dummy_list1 = list()
for i in optimizers:
    dummy_list2 = list()
    for j in activation_functions:
        output = model_compile_fit_predict(i,j,epochs=5,filepath = '/home/bae/MidasProject/ML/ML_file/'+i+'_'+j)
        dummy_list2.append(output)
    dummy_list1.append(dummy_list2)
        
output_2d = dummy_list1

因为这里我有一条错误消息

InvalidArgumentError                      Traceback (most recent call last)
Input In [17], in <cell line: 2>()
      3 dummy_list2 = list()
      4 for j in activation_functions:
----> 5     output = model_compile_fit_predict(i,j,epochs=5, filepath = '/home/bae/MidasProject/ML/ML_file/'+i+'_'+j)
      6     dummy_list2.append(output)
      7 dummy_list1.append(dummy_list2)

Input In [15], in model_compile_fit_predict(opt, activation, epochs, filepath)
      7 model.add(tf.keras.layers.Dense(2, activation = activation))
     10 model.compile(loss = 'mse', optimizer = opt, 
     11           metrics = ['accuracy',tf.keras.metrics.Precision(),tf.keras.metrics.Recall()])
---> 13 hist = model.fit(x_train, y_train, epochs = epochs, validation_data = (x_val, y_val))
     15 loss, accuracy, precision, recall_1 = model.evaluate(x_test, y_test)
     16 y_predict = model.predict(x_test)

File ~/.local/lib/python3.10/site-packages/keras/utils/traceback_utils.py:67, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     65 except Exception as e:  # pylint: disable=broad-except
     66   filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67   raise e.with_traceback(filtered_tb) from None
     68 finally:
     69   del filtered_tb

File ~/.local/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:54, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     52 try:
     53   ctx.ensure_initialized()
---> 54   tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     55                                       inputs, attrs, num_outputs)
     56 except core._NotOkStatusException as e:
     57   if name is not None:

InvalidArgumentError: Graph execution error:

Detected at node 'assert_less_equal/Assert/AssertGuard/Assert' defined at (most recent call last):
    File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
      return _run_code(code, main_globals, None,
    File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
      exec(code, run_globals)
    File "/home/bae/.local/lib/python3.10/site-packages/ipykernel_launcher.py", line 17, in <module>
      app.launch_new_instance()
    File "/home/bae/.local/lib/python3.10/site-packages/traitlets/config/application.py", line 976, in launch_instance
      app.start()
    File "/home/bae/.local/lib/python3.10/site-packages/ipykernel/kernelapp.py", line 712, in start
      self.io_loop.start()
    File "/home/bae/.local/lib/python3.10/site-packages/tornado/platform/asyncio.py", line 215, in start
      self.asyncio_loop.run_forever()
    File "/usr/lib/python3.10/asyncio/base_events.py", line 600, in run_forever
      self._run_once()
    File "/usr/lib/python3.10/asyncio/base_events.py", line 1896, in _run_once
      handle._run()
    File "/usr/lib/python3.10/asyncio/events.py", line 80, in _run
      self._context.run(self._callback, *self._args)
    File "/home/bae/.local/lib/python3.10/site-packages/ipykernel/kernelbase.py", line 510, in dispatch_queue
      await self.process_one()
    File "/home/bae/.local/lib/python3.10/site-packages/ipykernel/kernelbase.py", line 499, in process_one
      await dispatch(*args)
    File "/home/bae/.local/lib/python3.10/site-packages/ipykernel/kernelbase.py", line 406, in dispatch_shell
      await result
    File "/home/bae/.local/lib/python3.10/site-packages/ipykernel/kernelbase.py", line 730, in execute_request
      reply_content = await reply_content
    File "/home/bae/.local/lib/python3.10/site-packages/ipykernel/ipkernel.py", line 383, in do_execute
      res = shell.run_cell(
    File "/home/bae/.local/lib/python3.10/site-packages/ipykernel/zmqshell.py", line 528, in run_cell
      return super().run_cell(*args, **kwargs)
    File "/home/bae/.local/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 2881, in run_cell
      result = self._run_cell(
    File "/home/bae/.local/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 2936, in _run_cell
      return runner(coro)
    File "/home/bae/.local/lib/python3.10/site-packages/IPython/core/async_helpers.py", line 129, in _pseudo_sync_runner
      coro.send(None)
    File "/home/bae/.local/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3135, in run_cell_async
      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
    File "/home/bae/.local/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3338, in run_ast_nodes
      if await self.run_code(code, result, async_=asy):
    File "/home/bae/.local/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3398, in run_code
      exec(code_obj, self.user_global_ns, self.user_ns)
    File "/tmp/ipykernel_25723/2605155955.py", line 5, in <cell line: 2>
      output = model_compile_fit_predict(i,j,epochs=5, filepath = '/home/bae/MidasProject/ML/ML_file/'+i+'_'+j)
    File "/tmp/ipykernel_25723/817902483.py", line 13, in model_compile_fit_predict
      hist = model.fit(x_train, y_train, epochs = epochs, validation_data = (x_val, y_val))
    File "/home/bae/.local/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 64, in error_handler
      return fn(*args, **kwargs)
    File "/home/bae/.local/lib/python3.10/site-packages/keras/engine/training.py", line 1409, in fit
      tmp_logs = self.train_function(iterator)
    File "/home/bae/.local/lib/python3.10/site-packages/keras/engine/training.py", line 1051, in train_function
      return step_function(self, iterator)
    File "/home/bae/.local/lib/python3.10/site-packages/keras/engine/training.py", line 1040, in step_function
      outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/home/bae/.local/lib/python3.10/site-packages/keras/engine/training.py", line 1030, in run_step
      outputs = model.train_step(data)
    File "/home/bae/.local/lib/python3.10/site-packages/keras/engine/training.py", line 894, in train_step
      return self.compute_metrics(x, y, y_pred, sample_weight)
    File "/home/bae/.local/lib/python3.10/site-packages/keras/engine/training.py", line 987, in compute_metrics
      self.compiled_metrics.update_state(y, y_pred, sample_weight)
    File "/home/bae/.local/lib/python3.10/site-packages/keras/engine/compile_utils.py", line 501, in update_state
      metric_obj.update_state(y_t, y_p, sample_weight=mask)
    File "/home/bae/.local/lib/python3.10/site-packages/keras/utils/metrics_utils.py", line 70, in decorated
      update_op = update_state_fn(*args, **kwargs)
    File "/home/bae/.local/lib/python3.10/site-packages/keras/metrics/base_metric.py", line 140, in update_state_fn
      return ag_update_state(*args, **kwargs)
    File "/home/bae/.local/lib/python3.10/site-packages/keras/metrics/metrics.py", line 818, in update_state
      return metrics_utils.update_confusion_matrix_variables(
    File "/home/bae/.local/lib/python3.10/site-packages/keras/utils/metrics_utils.py", line 606, in update_confusion_matrix_variables
      tf.debugging.assert_less_equal(
Node: 'assert_less_equal/Assert/AssertGuard/Assert'
assertion failed: [predictions must be <= 1] [Condition x <= y did not hold element-wise:] [x (sequential/dense_3/Relu:0) = ] [[27222.8672 37717.7305][41931.543...]...] [y (Cast_7/x:0) = ] [1]
     [[{{node assert_less_equal/Assert/AssertGuard/Assert}}]] [Op:__inference_train_function_1350]

我用的是两台电脑

MacBook Pro : macOS Monterey 12.4
          Intel(R) Core(TM) i5-1038NG7 CPU @ 2.00GHz, 
          Intel Iris Plus Graphics 1536 MB, 
Desktop : Ubuntu 22.04
      Intel i7-11700
      NVIDIA RTX 3070

我尝试了所有可能的组合

在两台电脑上，

有GPU，没有GPU，

在conda环境中(使用python 3.9)，在我的主目录(python3.10)中

重新安装tensorflow对我没用。

Answer 1

感谢上面评论的@Dr.Snoopy和@GiorgosLivanos，

问题是我使用损失函数和度量来进行分类，而不是我所做的回归。

model.compile(loss = "mean_absolute_percentage_error", optimizer = optimizer, 
              metrics = ['custom_metrics'])

应该像这样改变！