kilograms_trees = tf.data.experimental.CsvDataset(
        filenames='dataset/agrumeto.csv',
        record_defaults=[tf.float32],
        field_delim=",",
        header=True)

kilo_train = kilograms_trees.take(35)
kilo_test = kilograms_trees.skip(35)


def create_conv_layer(input):
    x = tf.keras.layers.Conv2D(32, (7, 7), activation='relu')(input)
    x = tf.keras.layers.MaxPooling2D((2, 2), (2,2))(x)
    x = tf.keras.Model(inputs=input, outputs=x)
    return x

inputA = tf.keras.Input(shape=(size,size,3))
inputB = tf.keras.Input(shape=(size,size,3))
inputC = tf.keras.Input(shape=(size,size,3))
inputD = tf.keras.Input(shape=(size,size,3))


x = create_conv_layer(inputA)
y = create_conv_layer(inputB)
w = create_conv_layer(inputC)
z = create_conv_layer(inputD)

# combine the output of the two branches
combined = tf.keras.layers.concatenate([x.output, y.output, w.output, z.output])

layer_1 = tf.keras.layers.Conv2D(16, (3,3), activation="relu")(combined)
layer_1 = tf.keras.layers.MaxPooling2D((2, 2))(layer_1)

layer_2 = tf.keras.layers.Conv2D(16, (3,3), activation="relu")(layer_1)
layer_2 = tf.keras.layers.MaxPooling2D((2, 2), (2,2))(layer_2)

layer_3 = tf.keras.layers.Conv2D(32, (3,3), activation="relu")(layer_2)
layer_3 = tf.keras.layers.MaxPooling2D((2, 2), (2,2))(layer_3)

layer_4 = tf.keras.layers.Conv2D(32, (3,3), activation="relu")(layer_3)
layer_4 = tf.keras.layers.MaxPooling2D((2, 2), (2,2))(layer_4)

flatten = tf.keras.layers.Flatten()(layer_4)
hidden1 = tf.keras.layers.Dense(10, activation='relu')(flatten)
output = tf.keras.layers.Dense(1, activation='relu')(hidden1)

model = tf.keras.Model(inputs=[x.input, y.input, w.input, z.input], outputs=output)

print(model.summary())

model.compile(optimizer='adam',
              loss="mean_absolute_percentage_error")

print("[INFO] training model...")
model.fit([trainA, trainB, trainC, trainD], kilo_train, epochs=5, batch_size=4)

test_loss, test_acc = model.evaluate([testA, testB, testC, testD], kilo_test)

print(test_acc)

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.40.04    Driver Version: 418.40.04    CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 1050    On   | 00000000:01:00.0 Off |                  N/A |
| N/A   54C    P0    N/A /  N/A |   3830MiB /  4042MiB |      8%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0       909      C   ...ycharmProjects/agrumeto/venv/bin/python  3159MiB |
|    0      1729      G   /usr/lib/xorg/Xorg                            27MiB |
|    0      1870      G   /usr/bin/gnome-shell                          69MiB |
|    0      6290      G   /usr/lib/xorg/Xorg                           273MiB |
|    0      6420      G   /usr/bin/gnome-shell                         127MiB |
|    0      6834      G   ...quest-channel-token=6261236721362009153    85MiB |
|    0      8806      G   ...pycharm-professional/132/jre64/bin/java     2MiB |
|    0     12830      G   ...-token=60E939FEF0A8E3D5C46B3D6911048536    31MiB |
|    0     27478      G   ...-token=ECA4D3D9ADD8448674D34492E89E40E3    51MiB |
+-----------------------------------------------------------------------------+

conv2d_7 (Conv2D)               (None, 14, 14, 32)   9248        max_pooling2d_6[0][0]
__________________________________________________________________________________________________
max_pooling2d_7 (MaxPooling2D)  (None, 7, 7, 32)     0           conv2d_7[0][0]
__________________________________________________________________________________________________
flatten (Flatten)               (None, 1568)         0           max_pooling2d_7[0][0]
__________________________________________________________________________________________________
dense (Dense)                   (None, 10)           15690       flatten[0][0]
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 1)            11          dense[0][0]
==================================================================================================
Total params: 69,301
Trainable params: 69,301
Non-trainable params: 0
__________________________________________________________________________________________________
None
[INFO] training model...