Build a QuantumCogGen model that combines cognitive capabilities, genetic principles, and quantum-inspired elements.

quantum_cog_gen.py

@@ -0,0 +1,54 @@
+```python
+import tensorflow as tf
+from tensorflow.keras.layers import Dense, Conv2D, LSTM, Attention, SelfAttention
+from tensorflow.keras.models import Model
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.losses import CategoricalCrossentropy
+
+class QuantumCogGen(Model):
+    def __init__(self, num_classes):
+        super(QuantumCogGen, self).__init__()
+        self.genetic_layer = Dense(64, activation='sigmoid')
+        self.liquid_layer = Conv2D(32, kernel_size=(3, 3), activation='relu')
+        self.generational_layer = LSTM(128, return_sequences=True)
+        self.conv_cognitive_layer = Conv2D(64, kernel_size=(3, 3), activation='relu')
+        self.recurrent_cognitive_layer = LSTM(64, return_sequences=True)
+        self.attentive_layer = Attention()
+        self.adversarial_layer = Dense(32, activation='relu')
+        self.progressive_layer = Dense(16, activation='relu')
+        self.quantum_layer = Dense(32, activation='tanh')
+        self.self_reflection_layer = Dense(64, activation='relu')
+        self.self_attention_layer = SelfAttention(64)
+        self.emotional_layer = Dense(32, activation='relu')
+        self.logic_reasoning_layer = Dense(16, activation='relu')
+        self.output_layer = Dense(num_classes, activation='softmax')
+
+    def call(self, inputs):
+        x = self.genetic_layer(inputs)
+        x = self.liquid_layer(x)
+        x = self.generational_layer(x)
+        x = self.conv_cognitive_layer(x)
+        x = self.recurrent_cognitive_layer(x)
+        x = self.attentive_layer(x)
+        x = self.adversarial_layer(x)
+        x = self.progressive_layer(x)
+        x = self.quantum_layer(x)
+        x = self.self_reflection_layer(x)
+        x = self.self_attention_layer(x)
+        x = self.emotional_layer(x)
+        x = self.logic_reasoning_layer(x)
+        return self.output_layer(x)
+
+# Example usage
+quantum_cog_gen = QuantumCogGen(num_classes=10)
+optimizer = Adam(learning_rate=0.001)
+loss_fn = CategoricalCrossentropy()
+
+# Training loop
+for epoch in range(num_epochs):
+    with tf.GradientTape() as tape:
+        logits = quantum_cog_gen(inputs)
+        loss_value = loss_fn(labels, logits)
+    grads = tape.gradient(loss_value, quantum_cog_gen.trainable_variables)
+    optimizer.apply_gradients(zip(grads, quantum_cog_gen.trainable_variables))
+```

shared_dependencies.md

@@ -0,0 +1,40 @@
+Shared dependencies between the generated files:
+
+- tensorflow
+- Dense
+- Conv2D
+- LSTM
+- Attention
+- SelfAttention
+- Model
+- Adam
+- CategoricalCrossentropy
+- QuantumCogGen
+- num_classes
+- genetic_layer
+- liquid_layer
+- generational_layer
+- conv_cognitive_layer
+- recurrent_cognitive_layer
+- attentive_layer
+- adversarial_layer
+- progressive_layer
+- quantum_layer
+- self_reflection_layer
+- self_attention_layer
+- emotional_layer
+- logic_reasoning_layer
+- output_layer
+- call
+- inputs
+- x
+- optimizer
+- learning_rate
+- loss_fn
+- logits
+- loss_value
+- grads
+- trainable_variables
+- apply_gradients
+- zip
+- num_epochs