## Issue

I have been trying to use 1D CNN to do simple classification problems. Such as creating a tabular data in csv and input it into python to do some simple classifications. First 31 columns of the data are the features and the last column is the condition. I have been doing classification with other ML method such as Lightgbm and Randomforest. I want to try using 1D CNN and see whether the accuracy can be improved.

```
X = raw_data[feature_names]
P = predict_data_raw[feature_names]
P1 = predict_data_raw[feature_names1]
y = raw_data['Conditions']
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=22, test_size=0.1)
model = Sequential()
model.add(Conv1D(filters=32, kernel_size=3, activation='relu'))
model.add(LayerNormalization())
model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
model.add(LayerNormalization())
model.add(GlobalAveragePooling1D())
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='loss_function', optimizer='adam', metrics=['accuracy'])
```

I want to output the prediction results and the prediction probabilities of the conditions. However, the training stuck at some points and show this error:

```
ValueError: Exception encountered when calling layer "sequential_26" (type Sequential).
Input 0 of layer "conv1d_33" is incompatible with the layer: expected min_ndim=3, found ndim=2. Full shape received: (None, 31)
Call arguments received by layer "sequential_26" (type Sequential):
• inputs=tf.Tensor(shape=(None, 31), dtype=float64)
• training=True
• mask=None
```

## Solution

`Conv1D`

expects a 3-dimensional input, while your input is just 2-dimensional. You can reshape your data or add a `Reshape`

layer:

```
model = Sequential()
model.add(Reshape((31, 1))
...
```

You might need to add an `input_shape`

Answered By – AndrzejO

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