Keras Sequential模型报错：'Sequential' object has no attribute 'ndim'求助

Hey there! This error is definitely a code issue—no internal Keras bug here. Let's break down what's going wrong and fix it step by step.

What's Causing the Error? #

Take a look at your training line:

f.fit(d,f,epochs=20,batch_size=10)

The fit() method expects the second argument to be your label/target data (the y values your model is supposed to predict), but you've passed your Sequential model instance f instead. Keras tries to treat this model as a tensor of training labels, but models don't have the ndim attribute (this is a property of numpy arrays/tensors that Keras uses to validate input shapes). That's exactly why you're hitting the AttributeError.

Step-by-Step Fixes #

1. Correct the fit() arguments
   Replace the second f with your actual label data variable. For example, if your labels are stored in a variable named y, your line should be:

   f.fit(d, y, epochs=20, batch_size=10)
   

2. Verify your data shapes
   Make sure your input data and labels match the model's expectations:

   • Your input d should have a shape of (number_of_samples, 9) (matching your first Dense layer's input_shape=(9,)).
   • Your label data y should have a shape of (number_of_samples, 1) (aligning with your final Dense layer's 1-neuron output for binary classification).
     You can check shapes quickly with:

   print("Input data shape:", d.shape)
   print("Label data shape:", y.shape)
   

3. Double-check imports (a quick sanity check)
   Ensure you've imported the necessary components correctly at the top of your script:

   from tensorflow.keras.models import Sequential
   from tensorflow.keras.layers import Dense
   

That should resolve the error and get your model training smoothly!

内容的提问来源于stack exchange，提问作者pirateking