值错误：图层 "sequential" 的输入 0 与图层不兼容：预期形状=(无，81)，找到的形状=(无，77)

Question

我正在尝试训练一个神经网络，但我得到以下错误:

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，但我无法这样做。有人能帮帮我吗?

下面是相同

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])

根据建议，我将代码编辑为:

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性时，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)
~AppDataLocalTemp/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)
~Anaconda3libsite-packageskerasutilstraceback_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
~Anaconda3libsite-packagestensorflowpythonframeworkfunc_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:UserssadikAnaconda3libsite-packageskerasenginetraining.py", line 1525, in test_function  *
return step_function(self, iterator)
File "C:UserssadikAnaconda3libsite-packageskerasenginetraining.py", line 1514, in step_function  **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:UserssadikAnaconda3libsite-packageskerasenginetraining.py", line 1507, in run_step  **
outputs = model.test_step(data)
File "C:UserssadikAnaconda3libsite-packageskerasenginetraining.py", line 1471, in test_step
y_pred = self(x, training=False)
File "C:UserssadikAnaconda3libsite-packageskerasutilstraceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:UserssadikAnaconda3libsite-packageskerasengineinput_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)

Answer 1

问题是您的输入数据不具有您在第一层中定义的相同形状。确保数据的特征维度对应于模型第一层中的输入形状。下面是一个例子:

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)

所以，如果你的特征维度是77，那么把input_shape=(81,)改为input_shape=(77,)。