I’m trying to input 13 integer numbers to a neural network and output 1 number depending on the 13 input numbers so I made a Flatten of input shape 13 and a Dense layer of 64 and output Dense layer with 1 neuron without any activation on any layer and I have 7 examples with 13 number for every example and 7 numbers for the corresponding output how to make the array of inputs and outputs

here is my code

```
`import numpy as np
```

from tensorflow.keras.layers import Input, Conv2D, Dense, Flatten, Dropout, GlobalMaxPooling2D, MaxPooling2D, BatchNormalization

from tensorflow.keras.models import Model, Sequential

from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint

xs = np.array([[2,9,5,0,5,0,6,2,4,0,3,0,8],

[2,9,6,0,9,0,7,2,4,0,3,6,9],

[2,9,7,0,5,0,2,1,3,0,0,4,2],

[2,7,0,1,0,3,1,2,7,0,0,7,0],

[2,7,0,0,9,2,7,3,1,0,0,0,6],

[2,6,9,0,6,1,7,2,7,0,1,5,5],

[2,4,6,0,4,2,3,2,4,0,0,4,8]], dtype=float).T

ys = np.array([[3,3,2,8,6,5,3]], dtype=float)

print('xs shape = ', xs.shape)

print('ys shape = ', ys.shape)

model = Sequential([

Flatten(input_shape=[xs.shape[0]]),

Dense(64),

Dense(64),

Dense(1)

])

model.compile (optimizer=‘sgd’, loss=‘mean_squared_error’)

early_stopping = EarlyStopping(monitor=‘val_loss’, patience=50)

model.summary()

model.fit(xs, ys, epochs = 1000, validation_split=0.2, callbacks=[early_stopping])

model.save(‘keras_model.h5’)`

and I got this error