层顺序的输入 0 与层不兼容：输入形状的预期轴 -1 具有值 8，但收到的输入具有形状（无，71）

Question

I'm new to NN. Can anybody help me to find out the error of this code?

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.losses import sparse_categorical_crossentropy
from tensorflow.keras.optimizers import Adam
from sklearn.model_selection import KFold
from numpy import loadtxt
import numpy as np
import pandas as pd

from google.colab import files
uploaded = files.upload()

dataset = loadtxt('mod_dfn.csv', delimiter=',')

X = dataset[:,0:71]
y = dataset[:,71]

kfold = KFold(n_splits=10, shuffle=True)

fold_no = 1
for train, test in kfold.split(X, y):

  model = Sequential()
  model.add(Dense(12, input_dim=8, activation='relu'))
  model.add(Dense(8, activation='relu'))
  model.add(Dense(1, activation='sigmoid'))

  model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

  print('------------------------------------------------------------------------')
  print(f'Training for fold {fold_no} ...')

  history = model.fit(X[train], y[train], batch_size=10, epochs=150, verbose=0)

  scores = model.evaluate(X[test], y[test], verbose=0)
  print(f'Score for fold {fold_no}: {model.metrics_names[0]} of {scores[0]}; {model.metrics_names[1]} of {scores[1]*100}%')
  acc_per_fold.append(scores[1] * 100)
  loss_per_fold.append(scores[0])

  fold_no = fold_no + 1

I'm getting this error

------------------------------------------------------------------------
Training for fold 1 ...
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-16-4ad6d644594b> in <module>()
     17 
     18   # Fit data to model
---> 19   history = model.fit(X[train], y[train], batch_size=10, epochs=150, verbose=0)
     20 
     21   # Generate generalization metrics

9 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    992           except Exception as e:  # pylint:disable=broad-except
    993             if hasattr(e, "ag_error_metadata"):
--> 994               raise e.ag_error_metadata.to_exception(e)
    995             else:
    996               raise

ValueError: in user code:

    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:853 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:842 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:835 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:787 train_step
        y_pred = self(x, training=True)
    /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:1020 __call__
        input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
    /usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py:254 assert_input_compatibility
        ' but received input with shape ' + display_shape(x.shape))

    ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 8 but received input with shape (None, 71)

Answer 1

From comments

You are passing a data with 71 features (X=[:,0:71]) while you have specified in your first layers input features as 8 (input_dim=8) . Change input dim to input_dim=71 .

If your last layer has 1 output as a binary, then last dimension of Y also should be 1 .

(Paraphrased from Kaveh)