```python #!pip3 install tensorflow import tensorflow as tf import numpy as np import matplotlib.pyplot as plt ``` ```python # data for training the ann mode # option 1: celsius = np.array([-40, -10, 0, 8, 15, 22, 38], dtype=float) fahrenheit = np.array([-40, 14, 32, 46, 59, 72, 100], dtype=float) # option 2: (X°C x 9/5) + 32 = 41 °F points = 1000 np.random.seed(99) dataIn = np.linspace (-40,60, points) target = dataIn*9/5 + 32 +4*np.random.randn(points) plt.plot(celsius, fahrenheit, 'or', label='data-set 1') plt.plot(dataIn, target, '.b', alpha=0.3, label='data-set 2') plt.legend() plt.grid() plt.show() ``` ![png](Readme_files/Readme_1_0.png) ```python from tensorflow.keras.models import Sequential # ANN type from tensorflow.keras.layers import Dense, Input # All nodes connected # NN definition hn=2 model = Sequential() model.add(Input(shape=(1,), name='input')) model.add(Dense(hn, activation='linear', name='hidden')) model.add(Dense(1, activation='linear', name='output')) model.summary() ```

Model: "sequential_1"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ hidden (Dense)                  │ (None, 2)              │             4 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ output (Dense)                  │ (None, 1)              │             3 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 7 (28.00 B)

 Trainable params: 7 (28.00 B)

 Non-trainable params: 0 (0.00 B)

```python ### veri important note implement a python code # to show the ANN model connection using ascii ``` ```python from tensorflow.keras.optimizers import Adam #hyper parameters epoch = 500 lr = 0.01 tf.random.set_seed(99) # For TensorFlow model.compile(optimizer=Adam(lr), loss='mean_squared_error') print("Starting training ...") historial = model.fit(dataIn, target, epochs=epoch, verbose=False,) print("Model trainned!") ``` Starting training ... Model trainned! ```python plt.plot(historial.epoch, historial.history['loss'], '.k' ) plt.show() ``` ![png](Readme_files/Readme_5_0.png) ```python predict = model.predict(dataIn) plt.plot(dataIn, predict, '-r', label='estimated') plt.plot(dataIn,target, '.b', label='real', alpha=0.1) #plt.xlim([0, 20]) #plt.ylim([32, 39]) plt.legend() plt.grid() plt.show() ``` [1m32/32[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 741us/step ![png](Readme_files/Readme_6_1.png) ```python for layer in model.layers: print(layer.get_weights()) ``` [array([[ 0.7760521 , -0.18955402]], dtype=float32), array([8.428659, 8.034532], dtype=float32)] [array([[2.4613197 ], [0.63733613]], dtype=float32), array([6.2560296], dtype=float32)] ```python # Get weights for layer in model.layers: weights = layer.get_weights() print(f"Layer: {layer.name}") print(f" Weights (Kernel): {weights[0].shape} \n{weights[0]}") print(f" Biases: {weights[1].shape} \n{weights[1]}") ``` Layer: hidden Weights (Kernel): (1, 2) [[ 0.7760521 -0.18955402]] Biases: (2,) [8.428659 8.034532] Layer: output Weights (Kernel): (2, 1) [[2.4613197 ] [0.63733613]] Biases: (1,) [6.2560296] ```python inData = np.array([100]) model.predict(inData) ``` [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/step array([[211.05261]], dtype=float32) ```python wih = np.array([[ 0.7760521, -0.18955402]]) bh = np.array([8.428659, 8.034532]) Xh = np.dot(inData, wih) + bh who = np.array([[2.4613197 ],[0.63733613]]) bo = np.array([6.2560296]) O = np.dot(Xh,who) + bo O ``` array([211.05262121]) # Testing the model ```python inTest = np.array([100]) model.predict(inTest) ``` [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 22ms/step array([[115.929985]], dtype=float32) ```python # Do the Maths: inTest = np.array(inTest) whi = np.array([[-0.27738443, 0.7908125 ]]) bh = np.array([-8.219968, 6.714554]) Oh = np.dot(inTest,whi)+bh who = np.array([[-1.9934888],[ 1.5958738]]) bo = np.array([5.1361823]) Oo = np.dot(Oh,who)+bo Oo ``` array([213.73814765]) # sklearn ```python from sklearn.neural_network import MLPRegressor from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split import numpy as np # Datos de ejemplo # Escalado de los datos scaler_X = StandardScaler() scaler_y = StandardScaler() X_scaled = scaler_X.fit_transform(dataIn.reshape(-1,1)) y_scaled = scaler_y.fit_transform(target.reshape(-1, 1)).ravel() # Modelo equivalente al de Keras mlp = MLPRegressor( hidden_layer_sizes=(2,), # 1 capa oculta con 2 neuronas activation='identity', # activación lineal learning_rate_init=0.001, # 👈 Learning rate solver='adam', max_iter=1000, tol=1e-6, random_state=4 ) # Entrenar modelo mlp.fit(X_scaled, y_scaled) # Predicción y_pred_scaled = mlp.predict(X_scaled) y_pred = scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)) # Visualizar resultados (opcional) import matplotlib.pyplot as plt plt.scatter(dataIn, target, label="Original data") plt.plot(dataIn, y_pred, color='red', label="MLPRegressor output") plt.legend() plt.show() ``` ![png](Readme_files/Readme_15_0.png) ```python plt.plot(mlp.loss_curve_,'.k') plt.xlabel("Épocas") plt.ylabel("Error (loss)") plt.title("Evolución del error en entrenamiento") plt.grid(True) plt.show() ``` ![png](Readme_files/Readme_16_0.png) ```python print("Pesos entre capa de entrada y oculta:", mlp.coefs_[0]) print("Pesos entre capa oculta y salida:", mlp.coefs_[1]) print("Bias de capa oculta:", mlp.intercepts_[0]) print("Bias de salida:", mlp.intercepts_[1]) ``` Pesos entre capa de entrada y oculta: [[ 1.70549238 -0.37235861]] Pesos entre capa oculta y salida: [[ 0.30934654] [-1.25842791]] Bias de capa oculta: [1.02819949 1.02732046] Bias de salida: [0.97683886] # Model scheme ```python def generate_ascii_ann(model): ascii_diagram = "\nArtificial Neural Network Architecture:\n" for i, layer in enumerate(model.layers): weights = layer.get_weights() # Determine layer type and number of neurons if isinstance(layer, Dense): input_dim = weights[0].shape[0] # Number of inputs output_dim = weights[0].shape[1] # Number of neurons ascii_diagram += f"\nLayer {i+1}: {layer.name} ({layer.__class__.__name__})\n" ascii_diagram += f" Inputs: {input_dim}, Neurons: {output_dim}\n" ascii_diagram += f" Weights Shape: {weights[0].shape}\n" if len(weights) > 1: # If bias exists ascii_diagram += f" Biases Shape: {weights[1].shape}\n" # ASCII representation of neurons ascii_diagram += " " + " o " * output_dim + " <- Output Neurons\n" ascii_diagram += " | " * output_dim + "\n" ascii_diagram += " " + " | " * input_dim + " <- Inputs\n" return ascii_diagram # Generate and print the ASCII diagram ascii_ann = generate_ascii_ann(model) print(ascii_ann) ``` Artificial Neural Network Architecture: Layer 1: hidden (Dense) Inputs: 1, Neurons: 2 Weights Shape: (1, 2) Biases Shape: (2,) o o <- Output Neurons | | | <- Inputs Layer 2: output (Dense) Inputs: 2, Neurons: 1 Weights Shape: (2, 1) Biases Shape: (1,) o <- Output Neurons | | | <- Inputs ```mermaid graph LR I1((I_1)) --> H1((H_1)) & H2((H_1)) H1 & H2 --> O1((O_1)) & O2((O_2)) ```