I am trying to train a neural network but I am getting the following error:
ValueError: Input 0 of layer "sequential" is incompatible with the layer: expected shape=(None, 81), found shape=(None, 77)
I tried to find the solution to this but am unable to do so. Can someone please help me?
Here Is the code of the same
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])
As suggested I have edited the code to:
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)
But when I try to find the accuracy I get the same error as shown below:
# 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)
CodePudding user response：
The problem is that your input data does not have the same shape that you defined in your first layer. Make sure the features dimension of your data corresponds to the input shape in the model's first layer. Here is an example:
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)
So, if your feature dimension is 77 then change input_shape=(81,) to input_shape=(77,).