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[英]ValueError: Dimensions must be equal, but are 1 and 3 for 'Conv2D' (op: 'Conv2D') with input shapes: [1,400,400,1], [1,3,3,1]
[英]Keras ValueError: Dimensions must be equal, but are 9 and 400 for '{{node Equal}}' with input shapes: [?,9], [?,300,400]
我正在嘗試訓練一個非常簡單的 Keras 網絡來對一些保存為np.array
單熱編碼圖像進行分類。 輸入數據結構由一個.npy
文件組成,其中包含 500 張圖像(每張 3 個數組,因為它是 RGB)和一個帶有每個圖像的單熱編碼數組,以確定其分類。 每張圖像為 400x300 像素(寬 x 高),目標輸出應為 9 個類。 因此,每個圖像的形狀為(300, 400, 3)
並且每個單熱編碼標簽列表的長度為9
。
這是我目前使用的代碼:
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,當我嘗試運行model.fit()
,我總是面臨同樣的錯誤:
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].
我一直在閱讀 Keras 文檔和很多 SO 問題,但我無法弄清楚代碼有什么問題。 我認為問題可能出在輸入層的定義上,但我也嘗試過其他配置並返回錯誤。
提前致謝!!
最后,我找到了一個適用於系統的解決方案。 它的效率不是很高,因為它在運行時會消耗大量內存,但至少它可以運行。 問題在於模型本身,因為輸入的形狀(4D 數組。RGB 通道為 3 維,目標標簽為 1 維)與密集層不兼容。
在這種情況下,要定義一個 Dense 層,必須先創建一個 Flatten 層。 除此之外,我添加了一個 Conv2D 層作為第一個隱藏層。 因此,該模型如下所示:
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'))
整個腳本是這樣的:
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("============================")
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