My train set has 10 columns including a target column that I'm trying to predict, while my test set (`dataframe_test`) has 9 columns. When I run the code I receive this error:

```
Input 0 of layer "Hidden1" is incompatible with the layer: expected axis -1 of input shape to have value 10, but received input with shape (None, 9)

Call arguments received:
  • inputs=tf.Tensor(shape=(None, 9), dtype=float64)
  • training=False
  • mask=None**

```

My model looks like this:

```
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(units=10,
                                activation='relu',
                                kernel_regularizer=tf.keras.regularizers.l2(l=0.01),
                                name='Hidden1'))
model.add(tf.keras.layers.Dense(units=6,
                                activation='relu',
                                kernel_regularizer=tf.keras.regularizers.l2(l=0.01),
                                name='Hidden2'))

model.add(tf.keras.layers.Dense(units=1,
                                name='Output'))
my_learning_rate = 0.3    
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=my_learning_rate),
              loss="categorical_crossentropy",
              metrics='accuracy')

epochs = 10
batch_size = 32
history = model.fit(train, y_train, epochs = epochs, batch_size = batch_size)
epochs = history.epoch
print(epochs)
score = model.predict(dataframe_test)

```