我正试图在谷歌collab上创建一个CNN，但运行它时遇到了一个错误InvalidArgumentError:图形执行错误：
直到最后一行，一切都很好。我出错是有原因的吗？
from google.colab import drive
drive.mount('/content/drive')
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import numpy
import os
DIRECTORY = "/content/drive/MyDrive/TennisImages"
CLASS_MODE = "categorical"
COLOR_MODE = "rgb"
BATCH_SIZE = 32
training_data_generator = ImageDataGenerator(rescale=1.0/255, zoom_range=0.1, width_shift_range=0.05, height_shift_range=0.05, horizontal_flip = True)
validation_data_generator = ImageDataGenerator()
training_iterator = training_data_generator.flow_from_directory(DIRECTORY, class_mode = "categorical", color_mode = "rgb", batch_size = BATCH_SIZE)
training_iterator.next()
validation_iterator = validation_data_generator.flow_from_directory(DIRECTORY,class_mode='categorical', color_mode='rgb',batch_size=BATCH_SIZE)
def design_model(training_data):
# sequential model
model = Sequential()
# add input layer with rgb image shape
model.add(tf.keras.Input(shape=(414, 896, 3)))
model.add(layers.Conv2D(filters = 32, kernel_size = (5,5), activation = 'relu'))
model.add(layers.MaxPooling2D(2,2))
model.add(layers.Conv2D(filters = 64, kernel_size = (3,3), activation = 'relu'))
model.add(layers.MaxPooling2D(2,2))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation = 'relu'))
model.add(layers.Dense(2, activation = 'softmax'))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=.001), loss=tf.keras.losses.CategoricalCrossentropy(), metrics=[tf.keras.metrics.CategoricalAccuracy(),tf.keras.metrics.AUC()],)
# summarize model
model.summary()
return model
model = design_model(training_iterator)
history = model.fit(training_iterator, steps_per_epoch = 1, epochs = 5, validation_data = validation_iterator, validation_steps = 1)
错误消息为：如何编辑我的代码以便修复此错误？非常感谢。
InvalidArgumentError Traceback (most recent call last)
<ipython-input-19-fcce5cac1884> in <module>
2
3
----> 4 history = model.fit(training_iterator, steps_per_epoch = 1, epochs = 5, validation_data = validation_iterator, validation_steps = 1)
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
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 'sequential_3/flatten_3/Reshape' defined at (most recent call last):
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 846, in launch_instance
app.start()
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 612, in start
self.io_loop.start()
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 132, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
self._run_once()
File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
handle._run()
File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.7/dist-packages/tornado/ioloop.py", line 758, in _run_callback
ret = callback()
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 1233, in inner
self.run()
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 1147, in run
yielded = self.gen.send(value)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 365, in process_one
yield gen.maybe_future(dispatch(*args))
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 326, in wrapper
yielded = next(result)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 268, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 326, in wrapper
yielded = next(result)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 545, in execute_request
user_expressions, allow_stdin,
File "/usr/local/lib/python3.7/dist-packages/tornado/gen.py", line 326, in wrapper
yielded = next(result)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 306, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 536, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2855, in run_cell
raw_cell, store_history, silent, shell_futures)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2881, in _run_cell
return runner(coro)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/async_helpers.py", line 68, in _pseudo_sync_runner
coro.send(None)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3058, in run_cell_async
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3249, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 3326, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-19-fcce5cac1884>", line 4, in <module>
history = model.fit(training_iterator, steps_per_epoch = 1, epochs = 5, validation_data = validation_iterator, validation_steps = 1)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1384, in fit
tmp_logs = self.train_function(iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1000, in run_step
outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 859, in train_step
y_pred = self(x, training=True)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py", line 374, in call
return super(Sequential, self).call(inputs, training=training, mask=mask)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py", line 452, in call
inputs, training=training, mask=mask)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py", line 589, in _run_internal_graph
outputs = node.layer(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 64, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1096, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 92, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/keras/layers/core/flatten.py", line 96, in call
return tf.reshape(inputs, flattened_shape)
Node: 'sequential_3/flatten_3/Reshape'
Input to reshape is a tensor with 984064 values, but the requested shape requires a multiple of 1435008
[[{{node sequential_3/flatten_3/Reshape}}]] [Op:__inference_train_function_4377]
当您向fit方法提供数据集时，它不会检查图像大小。因此，它们毫无问题地通过conv层(它们接受任何图像大小(，但在Flatten层会有问题，因为基于之前的层和input_shape，它预计会有1435008个数字。所以它在平坦层失效。您可以通过提供正确大小的图像来解决此问题，将target_size=(414, 896)添加到flow_from_directory:
training_iterator = training_data_generator.flow_from_directory(DIRECTORY, target_size=(414, 896), class_mode = "categorical", color_mode = "rgb", batch_size = BATCH_SIZE)
training_iterator.next()
validation_iterator = validation_data_generator.flow_from_directory(DIRECTORY, target_size=(414, 896), class_mode='categorical', color_mode='rgb',batch_size=BATCH_SIZE)