本文介绍了Keras ValueError:尺寸必须相等，但"{{node Equal}}"的输入形状为[?，9]，[?, 300,400]为9和400的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在尝试训练一个非常简单的Keras网络，以对另存为np.array的一些一键编码图像进行分类.输入数据结构由一个.npy文件组成，具有500张图像(每张图像为RGB，每张3个阵列)和每个图像的一幅热编码阵列以确定其分类.每个图像为400x300像素(宽x高)，目标输出应为9类.因此，每张图像的形状为(300, 400, 3)，每张热编码标签列表的长度为9.
I'm trying to train a very simple Keras network to classify some one-hot encoded images saved as np.array. The input data structure is made of a .npy file, with 500 images (3 arrays each one, as it's RGB) and a one-hot encoded array with each image to determine it's classification. Each image is 400x300 pixels (Width x Height), and the target output should be of 9 classes. Hence, each image has a shape of (300, 400, 3) and each one-hot encoded label list has a length of 9.
这是我当前正在使用的代码:
This is the code that I am currently using:
import numpy as np
import cv2
import time
import os
import pandas as pd
from collections import deque
from random import shuffle
import pickle
# Do not display following messages:
# 0 = all messages are logged (default behavior)
# 1 = INFO messages are not printed
# 2 = INFO and WARNING messages are not printed
# 3 = INFO, WARNING, and ERROR messages are not printed
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
physical_devices = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0],True)
#os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
from keras import Input
from keras.models import load_model
lr = 0.01 # Learning Rate
WIDTH = 400
HEIGHT = 300
file_name = 'path/to/training_data.npy'
train_data = np.load(file_name, allow_pickle=True)
SAMPLE = len(train_data)
print('training_data.npy - Sample Size: {}'.format(SAMPLE))
X = np.array([i[0] for i in train_data]) / 255.0 # Divide to normalize values between 0 and 1
print('X shape: {}'.format(str(X.shape)))
Y = np.array([i[1] for i in train_data])
print("============================")
model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(WIDTH, HEIGHT, 3)))
model.add(tf.keras.layers.Dense(512, activation='relu'))
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(9, activation='softmax'))
model.compile(
optimizer = tf.keras.optimizers.SGD(lr=lr),
loss = 'mse',
metrics = ['acc']
)
model.summary()
model.fit(X, Y, epochs=5)
print("============================")
但是，当我尝试运行model.fit()时，总是遇到相同的错误:
However, when I try to run model.fit(), I always face the same error:
WARNING:tensorflow:Model was constructed with shape (None, 400, 300, 3) for input Tensor("input_1:0", shape=(None, 400, 300, 3), dtype=float32), but it was called on an input with incompatible shape (None, 300, 400, 3).
Traceback (most recent call last):
File "test.py", line 78, in <module>
model.fit(X, Y, epochs=5)
File "D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\keras\engine\training.py", line 108, in _method_wrapper
return method(self, *args, **kwargs)
File "D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1098, in fit
tmp_logs = train_function(iterator)
File "D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\eager\def_function.py", line 780, in __call__
result = self._call(*args, **kwds)
File "D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\eager\def_function.py", line 823, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\eager\def_function.py", line 697, in _initialize
*args, **kwds))
File "D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\eager\function.py", line 2855, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\eager\function.py", line 3213, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\eager\function.py", line 3075, in _create_graph_function
capture_by_value=self._capture_by_value),
File "D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\framework\func_graph.py", line 986, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\eager\def_function.py", line 600, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\framework\func_graph.py", line 973, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function *
return step_function(self, iterator)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step **
outputs = model.train_step(data)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\keras\engine\training.py:759 train_step
self.compiled_metrics.update_state(y, y_pred, sample_weight)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:409 update_state
metric_obj.update_state(y_t, y_p, sample_weight=mask)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\keras\utils\metrics_utils.py:90 decorated
update_op = update_state_fn(*args, **kwargs)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\keras\metrics.py:176 update_state_fn
return ag_update_state(*args, **kwargs)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\keras\metrics.py:612 update_state **
matches = ag_fn(y_true, y_pred, **self._fn_kwargs)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\keras\metrics.py:3309 sparse_categorical_accuracy
return math_ops.cast(math_ops.equal(y_true, y_pred), K.floatx())
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args, **kwargs)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\ops\math_ops.py:1613 equal
return gen_math_ops.equal(x, y, name=name)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\ops\gen_math_ops.py:3224 equal
name=name)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\framework\op_def_library.py:744 _apply_op_helper
attrs=attr_protos, op_def=op_def)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\framework\func_graph.py:593 _create_op_internal
compute_device)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\framework\ops.py:3485 _create_op_internal
op_def=op_def)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\framework\ops.py:1975 __init__
control_input_ops, op_def)
D:\Anaconda3\envs\pygta5_env_tf2.0\lib\site-packages\tensorflow\python\framework\ops.py:1815 _create_c_op
raise ValueError(str(e))
ValueError: Dimensions must be equal, but are 9 and 400 for '{{node Equal}} = Equal[T=DT_FLOAT, incompatible_shape_error=true](Cast_1, Cast_2)' with input shapes: [?,9], [?,300,400].
我一直在阅读Keras文档和许多SO问题，但是我无法弄清楚代码出了什么问题.我认为问题可能出在输入层的定义上，但我尝试了其他配置并返回错误.
I have been reading Keras documentation and lots of SO questions, but I can't figure out what's wrong with the code. I think that the problem might be located in the definiton of the input layer, but I have tried other configurations and returns errors as well.
提前谢谢！
推荐答案
最后，我找到了适用于该系统的解决方案.它效率不高，因为它在运行时会占用大量内存，但至少会运行.问题在于模型本身，因为输入的形状(4D阵列.RGB通道为3维，目标标签为1维)与密集层不兼容.
Finally, I've found a solution that works for the system. It's not very efficient as it consumes a lot of memory while running, but at least it runs. The problem was the model itself, as the shape of the input (4D array. 3 dimensions for RGB channels and 1 dimension for target labels) was incompatible with the dense layer.
在这种情况下，要定义密集层，必须先创建平坦层.除此之外，我还添加了一个Conv2D层作为第一个隐藏层.因此，该模型如下所示:
To define a Dense layer in this case, it's necessary to create previously a Flatten layer. Besides this, I've added a Conv2D layer as first hidden layer. Thus, the model looks like this:
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(filters=2, kernel_size=2, input_shape=(HEIGHT,WIDTH,3)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(9, activation='relu'))
整个脚本如下:
import numpy as np
import cv2
import time
import os
import pandas as pd
from collections import deque
from random import shuffle
import pickle
# Do not display following messages:
# 0 = all messages are logged (default behavior)
# 1 = INFO messages are not printed
# 2 = INFO and WARNING messages are not printed
# 3 = INFO, WARNING, and ERROR messages are not printed
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
physical_devices = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0],True)
#os.environ["KERAS_BACKEND"] = "plaidml.keras.backend"
from keras import Input
from keras.models import load_model
lr = 0.01 # Learning Rate
WIDTH = 400
HEIGHT = 300
file_name = 'path/to/training_data.npy'
train_data = np.load(file_name, allow_pickle=True)
SAMPLE = len(train_data)
print('training_data.npy - Sample Size: {}'.format(SAMPLE))
X = np.array([i[0] for i in train_data]) / 255.0 # Divide to normalize values between 0 and 1
print('X shape: {}'.format(str(X.shape)))
Y = np.array([i[1] for i in train_data])
print("============================")
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(filters=2, kernel_size=2, input_shape=(HEIGHT,WIDTH,3)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(9, activation='relu'))
model.compile(
optimizer = tf.keras.optimizers.SGD(lr=lr),
loss = 'mse',
metrics = ['acc']
)
model.summary()
model.fit(X, Y, epochs=5)
print("============================")
这篇关于Keras ValueError:尺寸必须相等，但"{{node Equal}}"的输入形状为[?，9]，[?, 300,400]为9和400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！