model - Copy.py

model - Copy.py

@@ -1,48 +0,0 @@
-# ==============================================
-# model.py  |  Residual Super-Resolution Model
-# ==============================================
-
-%%writefile model.py
-import tensorflow as tf
-from tensorflow.keras.models import Model
-from tensorflow.keras.layers import Input, Conv2D, Add, Activation
-
-
-def psnr(y_true, y_pred):
-    """
-    Computes the Peak Signal-to-Noise Ratio (PSNR) metric.
-    """
-    return tf.image.psnr(y_true, y_pred, max_val=1.0)
-
-
-def build_enhanced_model(input_shape=(32, 32, 3)):
-    """
-    Builds an enhanced residual model for image super-resolution.
-    """
-    # --- Input Layer ---
-    inputs = Input(shape=input_shape)
-
-    # --- Feature Extraction Layers ---
-    # Using smaller 3x3 kernels improves efficiency and generalization
-    x = Conv2D(64, (3, 3), padding='same', activation='relu')(inputs)
-    x = Conv2D(64, (3, 3), padding='same', activation='relu')(x)
-    x = Conv2D(64, (3, 3), padding='same', activation='relu')(x)
-    x = Conv2D(64, (3, 3), padding='same', activation='relu')(x)  # Extra depth for richer features
-
-    # --- Reconstruction Layer ---
-    x = Conv2D(3, (3, 3), padding='same')(x)  # No activation here (linear output)
-
-    # --- Residual Connection ---
-    # The model learns to predict the missing details (residuals)
-    outputs = Add()([inputs, x])
-    outputs = Activation('sigmoid')(outputs)  # Keeps pixel values in [0, 1]
-
-    # --- Build and Compile the Model ---
-    model = Model(inputs=inputs, outputs=outputs)
-    model.compile(
-        optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
-        loss='mean_squared_error',
-        metrics=[psnr]
-    )
-
-    return model