====== Influye el hardware en el resultado ======
Vamos a comprobar si usar distinto hardware influye en el resultado del entrenamiento.


<sxh python>
#!/usr/bin/env python3
import sys
import random
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.datasets import load_iris

print("Versión de Python:", sys.version)
print("Versión de NumPy:", np.__version__)
print("Versión de TensorFlow:", tf.__version__)

iris=load_iris()
x=iris.data[0:99,[0,2]]
y=iris.target[0:99]

random.seed(5) 
np.random.seed(5)
tf.random.set_seed(5)
 
model=Sequential()
model.add(Dense(5, activation='relu',input_dim=2))
model.add(Dense(5, activation='relu'))
model.compile(loss='mean_squared_error')
model.fit(x, y,epochs=200,verbose=False) 
for layer in model.layers:
    print(layer.get_weights()[0].reshape(-1)) 
for layer in model.layers:
    print(layer.get_weights()[1])
</sxh>


===== Tensorflow 2.12 =====


  * Mi ordenador con Tensorflow 2.12
<sxh base>
Versión de Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
Versión de NumPy: 1.23.5
Versión de TensorFlow: 2.12.0
[ 0.13083223  0.07442822 -0.2500074   0.47630367  0.5291078   0.31813693
  0.31183484  0.1446724  -0.7333437   0.18374144]
[ 0.6093438   0.08665708  0.31081778  0.2551267   0.05519938  0.4367431
 -0.7268674   0.50311357 -0.63850987  0.34656572  0.5320357  -0.48540664
 -0.5437882   0.58495295  0.42670572 -0.24922246 -0.9340179  -0.15385167
 -0.81791097 -0.13371903 -0.1941066   0.5657561   0.03889008  0.4567909
 -0.45787498]
[-0.2992502  -0.37618196  0.         -0.07332841 -0.29489776]
[ 0.00671715 -0.19779032 -0.40140766 -0.22690465  0.        ]
Predict: [[0.        0.        0.        0.        0.       ]
 [1.2197894 1.0331607 1.2148745 1.0691222 0.       ]]


</sxh>

<sxh base>
Versión de Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
Versión de NumPy: 1.23.5
Versión de TensorFlow: 2.12.0
[ 0.13083223  0.07442822 -0.2500074   0.47630367  0.5291078   0.31813693
  0.31183484  0.1446724  -0.7333437   0.18374144]
[ 0.6093438   0.08665708  0.31081778  0.2551267   0.05519938  0.4367431
 -0.7268674   0.50311357 -0.63850987  0.34656572  0.5320357  -0.48540664
 -0.5437882   0.58495295  0.42670572 -0.24922246 -0.9340179  -0.15385167
 -0.81791097 -0.13371903 -0.1941066   0.5657561   0.03889008  0.4567909
 -0.45787498]
[-0.2992502  -0.37618196  0.         -0.07332841 -0.29489776]
[ 0.00671715 -0.19779032 -0.40140766 -0.22690465  0.        ]
Predict: [[0.        0.        0.        0.        0.       ]
 [1.2197894 1.0331607 1.2148745 1.0691222 0.       ]]

</sxh>

  * Google Colab con TPU y tensorflow 2.12
<sxh base>
Versión de Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
Versión de NumPy: 1.23.5
Versión de TensorFlow: 2.12.0
[ 0.13083223  0.07442828 -0.2500074   0.4763036   0.5291076   0.31813705
  0.3118346   0.1446724  -0.7333438   0.18374145]
[ 0.60934395  0.08665702  0.3108179   0.25512677  0.05519938  0.43674305
 -0.72686714  0.5031137  -0.6385101   0.34656572  0.5320357  -0.48540664
 -0.5437882   0.58495295  0.42670572 -0.24922271 -0.9340177  -0.15385169
 -0.8179109  -0.13371903 -0.19410674  0.56575596  0.03888999  0.45679092
 -0.45787498]
[-0.29925007 -0.37618205  0.         -0.07332825 -0.29489756]
[ 0.00671728 -0.19779012 -0.4014075  -0.22690472  0.        ]
Predict: [[0.        0.        0.        0.        0.       ]
 [1.2197894 1.0331607 1.2148745 1.069122  0.       ]]
</sxh>

<sxh base>
Versión de Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
Versión de NumPy: 1.23.5
Versión de TensorFlow: 2.12.0
[ 0.13083223  0.07442828 -0.2500074   0.4763036   0.5291076   0.31813705
  0.3118346   0.1446724  -0.7333438   0.18374145]
[ 0.60934395  0.08665702  0.3108179   0.25512677  0.05519938  0.43674305
 -0.72686714  0.5031137  -0.6385101   0.34656572  0.5320357  -0.48540664
 -0.5437882   0.58495295  0.42670572 -0.24922271 -0.9340177  -0.15385169
 -0.8179109  -0.13371903 -0.19410674  0.56575596  0.03888999  0.45679092
 -0.45787498]
[-0.29925007 -0.37618205  0.         -0.07332825 -0.29489756]
[ 0.00671728 -0.19779012 -0.4014075  -0.22690472  0.        ]
Predict: [[0.        0.        0.        0.        0.       ]
 [1.2197894 1.0331607 1.2148745 1.069122  0.       ]]
</sxh>

===== Tensorflow 2.14 =====

  * Mi ordenador con Tensorflow 2.14
<sxh base>
Versión de Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
Versión de NumPy: 1.26.1
Versión de TensorFlow: 2.14.0
[ 0.13083228  0.07442826 -0.2500074   0.47630364  0.5291078   0.31813696
  0.31183475  0.1446724  -0.7333436   0.18374133]
[ 0.6093438   0.08665705  0.31081787  0.25512666  0.05519938  0.43674314
 -0.7268673   0.5031135  -0.6385097   0.34656572  0.5320357  -0.48540664
 -0.5437882   0.58495295  0.42670572 -0.24922258 -0.9340178  -0.15385169
 -0.8179109  -0.13371903 -0.19410668  0.5657561   0.03889009  0.4567909
 -0.45787498]
[-0.29925022 -0.37618196  0.         -0.07332844 -0.2948976 ]
[ 0.00671723 -0.19779022 -0.40140778 -0.22690488  0.        ]
Predict: [[0.        0.        0.        0.        0.       ]
 [1.2197893 1.0331608 1.2148747 1.0691222 0.       ]]
</sxh>

<sxh base>
Versión de Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
Versión de NumPy: 1.26.1
Versión de TensorFlow: 2.14.0
[ 0.13083228  0.07442826 -0.2500074   0.47630364  0.5291078   0.31813696
  0.31183475  0.1446724  -0.7333436   0.18374133]
[ 0.6093438   0.08665705  0.31081787  0.25512666  0.05519938  0.43674314
 -0.7268673   0.5031135  -0.6385097   0.34656572  0.5320357  -0.48540664
 -0.5437882   0.58495295  0.42670572 -0.24922258 -0.9340178  -0.15385169
 -0.8179109  -0.13371903 -0.19410668  0.5657561   0.03889009  0.4567909
 -0.45787498]
[-0.29925022 -0.37618196  0.         -0.07332844 -0.2948976 ]
[ 0.00671723 -0.19779022 -0.40140778 -0.22690488  0.        ]
Predict: [[0.        0.        0.        0.        0.       ]
 [1.2197893 1.0331608 1.2148747 1.0691222 0.       ]]


</sxh>

  * Google Colab con CPU y tensorflow 2.14
<sxh base>
Versión de Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
Versión de NumPy: 1.23.5
Versión de TensorFlow: 2.14.0
[ 0.13083225  0.07442821 -0.2500074   0.47630358  0.5291077   0.31813702
  0.3118347   0.1446724  -0.73334354  0.18374158]
[ 0.609344    0.08665705  0.31081787  0.25512683  0.05519938  0.43674326
 -0.72686714  0.5031137  -0.6385102   0.34656572  0.5320357  -0.48540664
 -0.5437882   0.58495295  0.42670572 -0.24922249 -0.93401766 -0.1538516
 -0.81791097 -0.13371903 -0.19410674  0.5657558   0.03889002  0.45679078
 -0.45787498]
[-0.2992501  -0.37618205  0.         -0.07332838 -0.29489738]
[ 0.00671722 -0.1977903  -0.40140763 -0.22690481  0.        ]
Predict: [[0.        0.        0.        0.        0.       ]
 [1.2197893 1.033161  1.2148744 1.0691221 0.       ]]
</sxh>

<sxh base>
Versión de Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
Versión de NumPy: 1.23.5
Versión de TensorFlow: 2.14.0
[ 0.13083225  0.07442821 -0.2500074   0.47630358  0.5291077   0.31813702
  0.3118347   0.1446724  -0.73334354  0.18374158]
[ 0.609344    0.08665705  0.31081787  0.25512683  0.05519938  0.43674326
 -0.72686714  0.5031137  -0.6385102   0.34656572  0.5320357  -0.48540664
 -0.5437882   0.58495295  0.42670572 -0.24922249 -0.93401766 -0.1538516
 -0.81791097 -0.13371903 -0.19410674  0.5657558   0.03889002  0.45679078
 -0.45787498]
[-0.2992501  -0.37618205  0.         -0.07332838 -0.29489738]
[ 0.00671722 -0.1977903  -0.40140763 -0.22690481  0.        ]
Predict: [[0.        0.        0.        0.        0.       ]
 [1.2197893 1.033161  1.2148744 1.0691221 0.       ]]
</sxh>

  * Google Colab con T4 GPU y tensorflow 2.14
<sxh base>
Versión de Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
Versión de NumPy: 1.23.5
Versión de TensorFlow: 2.14.0
[ 0.13083227  0.07442828 -0.2500074   0.4763036   0.5291076   0.318137
  0.3118347   0.1446724  -0.7333437   0.18374152]
[ 0.60934377  0.08665707  0.31081787  0.2551268   0.05519938  0.4367433
 -0.7268671   0.5031135  -0.6385101   0.34656572  0.5320357  -0.48540664
 -0.5437882   0.58495295  0.42670572 -0.24922267 -0.9340178  -0.15385167
 -0.817911   -0.13371903 -0.19410676  0.56575596  0.03889004  0.4567909
 -0.45787498]
[-0.29925022 -0.37618205  0.         -0.07332834 -0.29489756]
[ 0.00671724 -0.19779004 -0.4014077  -0.22690451  0.        ]
Predict: [[0.        0.        0.        0.        0.       ]
 [1.2197891 1.033161  1.2148743 1.0691222 0.       ]]
</sxh>
<sxh base>
Versión de Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
Versión de NumPy: 1.23.5
Versión de TensorFlow: 2.14.0
[ 0.13083221  0.07442825 -0.2500074   0.47630355  0.5291077   0.31813705
  0.31183478  0.1446724  -0.7333435   0.1837415 ]
[ 0.6093438   0.08665702  0.31081784  0.2551268   0.05519938  0.43674314
 -0.726867    0.50311357 -0.63851     0.34656572  0.5320357  -0.48540664
 -0.5437882   0.58495295  0.42670572 -0.24922241 -0.9340177  -0.15385164
 -0.8179108  -0.13371903 -0.1941067   0.565756    0.03889004  0.45679086
 -0.45787498]
[-0.29925022 -0.37618196  0.         -0.07332844 -0.29489744]
[ 0.00671725 -0.19779035 -0.40140772 -0.22690469  0.        ]
Predict: [[0.        0.        0.        0.        0.       ]
 [1.2197894 1.0331609 1.2148745 1.0691221 0.       ]]
</sxh>



===== Conclusión =====
Los pesos de las redes son similares aunque cambian un poquito en los últimos decimales y en los resultados de ''predict'' prácticamente son iguales.

Lo único extraño es que con "Google Colab con T4 GPU y tensorflow 2.14" distintas ejecuciones no daban exactamente el mismo resultado.