我正在尝试学习深度学习中的CNN模型，我使用Cats-vs_Dogs数据集开始。我正在跟随视频教程，步骤是相同的，尽管数据集不同，所有其他解决方案都有不同的代码，我无法理解。有人能告诉我为什么我在这里出错了吗?由于
import numpy as np
import pandas as pd
import tensorflow as tf
import itertools
import os
import shutil
import random
import glob
import matplotlib.pyplot as plt
import warnings
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense,Activation,Flatten,BatchNormalization,Conv2D,MaxPool2D
from tensorflow.keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix
train_batch=ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input)
.flow_from_directory(directory='/content/Cat-vs-Dogs/train',target_size=(244,244),classes=['cats','dogs'],batch_size=10)
valid_batch=ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input)
.flow_from_directory(directory='/content/Cat-vs-Dogs/valid',target_size=(244,244),classes=['cats','dogs'],batch_size=10)
test_batch=ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input)
.flow_from_directory(directory='/content/Cat-vs-Dogs/test',target_size=(244,244),classes=['cats','dogs'],batch_size=10,shuffle=False)
model1=Sequential([
Conv2D(filters=32,kernel_size=(3,3),activation='relu', padding='same',input_shape=(224,224,3)),
MaxPool2D(pool_size=(2,2),strides=2),
Conv2D(filters=64,kernel_size=(3,3),activation='relu', padding='same'),
MaxPool2D(pool_size=(2,2),strides=2),
Flatten(),
Dense(units=2,activation='softmax')
])
model1.summary()
model1.compile(optimizer=Adam(learning_rate=0.0001),loss='categorical_crossentropy',metrics=['accuracy'])
model1.fit(x=train_batch,validation_data=valid_batch,epochs=10,verbose=2)
```
The Error Output occurs like below
```
Epoch 1/10
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-87-3dd4821591bb> in <module>()
----> 1 model1.fit(x=train_batch,validation_data=valid_batch,epochs=10,verbose=2)
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_4/flatten_4/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 499, 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/platform/asyncio.py", line 122, in _handle_events
handler_func(fileobj, events)
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/zmq/eventloop/zmqstream.py", line 577, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 606, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 556, in _run_callback
callback(*args, **kwargs)
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/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 208, 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 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2828, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-76-3dd4821591bb>", line 1, in <module>
model1.fit(x=train_batch,validation_data=valid_batch,epochs=10,verbose=2)
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_4/flatten_4/Reshape'
Input to reshape is a tensor with 2381440 values, but the requested shape requires a multiple of 200704
[[{{node sequential_4/flatten_4/Reshape}}]] [Op:__inference_train_function_3624]
错误是由于目标尺寸为(244,244)，而模型给出的输入形状为(224,224,3)。您可以将目标尺寸更改为(224,224)或将输入形状更改为(244,244,3)。
将input_shape改为(244, 244, 3)
Conv2D(filters=32,kernel_size=(3,3),activation='relu', padding='same',input_shape=(244, 244, 3)),
或
将train_batch, valid_batch和test_batch中的target_size更改为(224, 224)
train_batch=ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input)
.flow_from_directory(directory='/content/Cat-vs-Dogs/train',target_size=(224, 224),classes=['cats','dogs'],batch_size=10)