model ok for lecture

2 weeks ago · dc2cf45b10
commit dc2cf45b10
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+.ipynb_checkpoints/
--- a/Readme.md
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+```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 = 100
+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()
+```
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold">Model: "sequential_1"</span>
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
+┃<span style="font-weight: bold"> Layer (type)                    </span>┃<span style="font-weight: bold"> Output Shape           </span>┃<span style="font-weight: bold">       Param # </span>┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
+│ hidden (<span style="color: #0087ff; text-decoration-color: #0087ff">Dense</span>)                  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">2</span>)              │             <span style="color: #00af00; text-decoration-color: #00af00">4</span> │
+├─────────────────────────────────┼────────────────────────┼───────────────┤
+│ output (<span style="color: #0087ff; text-decoration-color: #0087ff">Dense</span>)                  │ (<span style="color: #00d7ff; text-decoration-color: #00d7ff">None</span>, <span style="color: #00af00; text-decoration-color: #00af00">1</span>)              │             <span style="color: #00af00; text-decoration-color: #00af00">3</span> │
+└─────────────────────────────────┴────────────────────────┴───────────────┘
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Total params: </span><span style="color: #00af00; text-decoration-color: #00af00">7</span> (28.00 B)
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">7</span> (28.00 B)
+</pre>
+
+
+
+
+<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="font-weight: bold"> Non-trainable params: </span><span style="color: #00af00; text-decoration-color: #00af00">0</span> (0.00 B)
+</pre>
+
+
+
+
+```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
+hn = 2 # hidden nodes
+tf.random.set_seed(42)  # 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
+predict = model.predict(dataIn)
+plt.plot(dataIn, predict, ':r', label='estimated')
+plt.plot(dataIn,target, '.b', label='real', alpha=0.4)
+plt.legend()
+plt.grid()
+plt.show()
+```
+
+    [1m4/4[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 4ms/step 
+
+
+
+    
+![png](Readme_files/Readme_5_1.png)
+    
+
+
+
+```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) 
+    [[-0.27738443  0.7908125 ]]
+      Biases: (2,) 
+    [-8.219968  6.714554]
+    Layer: output
+      Weights (Kernel): (2, 1) 
+    [[-1.9934888]
+     [ 1.5958738]]
+      Biases: (1,) 
+    [5.1361823]
+
+
+# Testing the model
+
+
+```python
+inTest = np.array([100])
+model.predict(inTest)
+```
+
+    [1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 87ms/step
+
+
+
+
+
+    array([[213.73816]], 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])
+
+
+
+
+```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))
+```
--- a/Readme_files/Readme_1_0.png
+++ b/Readme_files/Readme_1_0.png
--- a/Readme_files/Readme_5_1.png
+++ b/Readme_files/Readme_5_1.png
--- a/main.ipynb
+++ b/main.ipynb