ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 81), found shape=(None, 77)
ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 81), found shape=(None, 77)
我正在尝试训练神经网络，但出现以下错误：
ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 81), found shape=(None, 77)
我试图找到解决这个问题的方法，但我无法做到。有人可以帮我吗？
这是相同的代码
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
from tensorflow.keras.callbacks import EarlyStopping
# Scaling the data
ss = StandardScaler()
X_train_sc = ss.fit_transform(X_train)
X_test_sc = ss.transform(X_test)
# Creating our model's structure
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(81,)))
model.add(Dropout(0.18))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(1, activation='sigmoid'))
es = EarlyStopping(monitor='val_loss', patience=5)
# Compiling the model
model.compile(loss='bce',
optimizer='adam',
metrics=['binary_accuracy'])
# Fitting the model
history = model.fit(X_train_sc,
y_train,
batch_size = 256,
validation_data =(X_test_sc, y_test),
epochs = 500,
verbose = 0,
callbacks=[es])
按照建议，我已将代码编辑为：
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
from tensorflow.keras.callbacks import EarlyStopping
import tensorflow as tf
samples = 500
X_train_sc = tf.random.normal((samples, 81))
y_train = tf.random.uniform((samples, ), maxval=2, dtype=tf.int32)
# Creating our model's structure
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(81,)))
model.add(Dropout(0.18))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(1, activation='sigmoid'))
es = EarlyStopping(monitor='val_loss', patience=5)
# Compiling the model
model.compile(loss='bce',
optimizer='adam',
metrics=['binary_accuracy'])
# Fitting the model
history = model.fit(X_train_sc,
y_train,
batch_size = 32,
epochs = 2,
verbose = 0)
但是当我试图找到准确度时，我得到了如下所示的相同错误：
# Scoring
train_score = model.evaluate(X_train_sc,
y_train,
verbose=1)
test_score = model.evaluate(X_test_sc,
y_test,
verbose=1)
labels = model.metrics_names
print('')
print(f'Training Accuracy: {train_score[1]}')
print(f'Testing Accuracy: {test_score[1]}')
16/16 [==============================] - 0s 2ms/step - loss: 0.6613 - binary_accuracy: 0.6040
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_7572/1082894889.py in <module>
3 y_train,
4 verbose=1)
----> 5 test_score = model.evaluate(X_test_sc,
6 y_test,
7 verbose=1)
~\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py in 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
~\Anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in autograph_handler(*args, **kwargs)
1145 except Exception as e: # pylint:disable=broad-except
1146 if hasattr(e, "ag_error_metadata"):
-> 1147 raise e.ag_error_metadata.to_exception(e)
1148 else:
1149 raise
ValueError: in user code:
File "C:\Users\sadik\Anaconda3\lib\site-packages\keras\engine\training.py", line 1525, in test_function *
return step_function(self, iterator)
File "C:\Users\sadik\Anaconda3\lib\site-packages\keras\engine\training.py", line 1514, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\sadik\Anaconda3\lib\site-packages\keras\engine\training.py", line 1507, in run_step **
outputs = model.test_step(data)
File "C:\Users\sadik\Anaconda3\lib\site-packages\keras\engine\training.py", line 1471, in test_step
y_pred = self(x, training=False)
File "C:\Users\sadik\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\sadik\Anaconda3\lib\site-packages\keras\engine\input_spec.py", line 264, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" is '
ValueError: Input 0 of layer "sequential_4" is incompatible with the layer: expected shape=(None, 81), found shape=(None, 77)
问题是您的输入数据与您在第一层中定义的形状不同。确保数据的特征维度对应于模型第一层中的输入形状。这是一个例子：
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
from tensorflow.keras.callbacks import EarlyStopping
import tensorflow as tf
samples = 500
# Create random dummy data
X_train_sc = tf.random.normal((samples, 81))
y_train = tf.random.uniform((samples, ), maxval=2, dtype=tf.int32)
X_test_sc = tf.random.normal((samples, 81))
y_test = tf.random.uniform((samples, ), maxval=2, dtype=tf.int32)
# Creating our model's structure
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(81,)))
model.add(Dropout(0.18))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(1, activation='sigmoid'))
# Compiling the model
model.compile(loss='bce',
optimizer='adam',
metrics=['binary_accuracy'])
# Fitting the model
history = model.fit(X_train_sc,
y_train,
batch_size = 32,
epochs = 2,
verbose = 0)
因此，如果您的特征维度是 77，则将 input_shape=(81,) 更改为 input_shape=(77,)。
我正在尝试训练神经网络，但出现以下错误：
ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 81), found shape=(None, 77)
我试图找到解决这个问题的方法，但我无法做到。有人可以帮我吗？
这是相同的代码
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
from tensorflow.keras.callbacks import EarlyStopping
# Scaling the data
ss = StandardScaler()
X_train_sc = ss.fit_transform(X_train)
X_test_sc = ss.transform(X_test)
# Creating our model's structure
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(81,)))
model.add(Dropout(0.18))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(1, activation='sigmoid'))
es = EarlyStopping(monitor='val_loss', patience=5)
# Compiling the model
model.compile(loss='bce',
optimizer='adam',
metrics=['binary_accuracy'])
# Fitting the model
history = model.fit(X_train_sc,
y_train,
batch_size = 256,
validation_data =(X_test_sc, y_test),
epochs = 500,
verbose = 0,
callbacks=[es])
按照建议，我已将代码编辑为：
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
from tensorflow.keras.callbacks import EarlyStopping
import tensorflow as tf
samples = 500
X_train_sc = tf.random.normal((samples, 81))
y_train = tf.random.uniform((samples, ), maxval=2, dtype=tf.int32)
# Creating our model's structure
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(81,)))
model.add(Dropout(0.18))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(1, activation='sigmoid'))
es = EarlyStopping(monitor='val_loss', patience=5)
# Compiling the model
model.compile(loss='bce',
optimizer='adam',
metrics=['binary_accuracy'])
# Fitting the model
history = model.fit(X_train_sc,
y_train,
batch_size = 32,
epochs = 2,
verbose = 0)
但是当我试图找到准确度时，我得到了如下所示的相同错误：
# Scoring
train_score = model.evaluate(X_train_sc,
y_train,
verbose=1)
test_score = model.evaluate(X_test_sc,
y_test,
verbose=1)
labels = model.metrics_names
print('')
print(f'Training Accuracy: {train_score[1]}')
print(f'Testing Accuracy: {test_score[1]}')
16/16 [==============================] - 0s 2ms/step - loss: 0.6613 - binary_accuracy: 0.6040
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_7572/1082894889.py in <module>
3 y_train,
4 verbose=1)
----> 5 test_score = model.evaluate(X_test_sc,
6 y_test,
7 verbose=1)
~\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py in 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
~\Anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in autograph_handler(*args, **kwargs)
1145 except Exception as e: # pylint:disable=broad-except
1146 if hasattr(e, "ag_error_metadata"):
-> 1147 raise e.ag_error_metadata.to_exception(e)
1148 else:
1149 raise
ValueError: in user code:
File "C:\Users\sadik\Anaconda3\lib\site-packages\keras\engine\training.py", line 1525, in test_function *
return step_function(self, iterator)
File "C:\Users\sadik\Anaconda3\lib\site-packages\keras\engine\training.py", line 1514, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\sadik\Anaconda3\lib\site-packages\keras\engine\training.py", line 1507, in run_step **
outputs = model.test_step(data)
File "C:\Users\sadik\Anaconda3\lib\site-packages\keras\engine\training.py", line 1471, in test_step
y_pred = self(x, training=False)
File "C:\Users\sadik\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\sadik\Anaconda3\lib\site-packages\keras\engine\input_spec.py", line 264, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" is '
ValueError: Input 0 of layer "sequential_4" is incompatible with the layer: expected shape=(None, 81), found shape=(None, 77)
问题是您的输入数据与您在第一层中定义的形状不同。确保数据的特征维度对应于模型第一层中的输入形状。这是一个例子：
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
from tensorflow.keras.callbacks import EarlyStopping
import tensorflow as tf
samples = 500
# Create random dummy data
X_train_sc = tf.random.normal((samples, 81))
y_train = tf.random.uniform((samples, ), maxval=2, dtype=tf.int32)
X_test_sc = tf.random.normal((samples, 81))
y_test = tf.random.uniform((samples, ), maxval=2, dtype=tf.int32)
# Creating our model's structure
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(81,)))
model.add(Dropout(0.18))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.15))
model.add(Dense(1, activation='sigmoid'))
# Compiling the model
model.compile(loss='bce',
optimizer='adam',
metrics=['binary_accuracy'])
# Fitting the model
history = model.fit(X_train_sc,
y_train,
batch_size = 32,
epochs = 2,
verbose = 0)
因此，如果您的特征维度是 77，则将 input_shape=(81,) 更改为 input_shape=(77,)。