Update unetlstm.py

unetlstm.py

@@ -1,243 +1,243 @@
-import tensorflow as tf
-from tensorflow.keras import layers, models  # type: ignore
-
-def encoder_block(inputs, filters):
-    x = layers.Conv3D(filters=filters, kernel_size=(3, 3, 4), padding="same", activation="relu")(inputs)
-    x = layers.BatchNormalization()(x)
-    return x
-
-def convlstm_block(inputs, filters):
-    # Reshape to (timesteps, height, width, channels) for ConvLSTM
-    x = layers.Reshape((inputs.shape[1], inputs.shape[2], inputs.shape[3], inputs.shape[4]))(inputs)
-    x = layers.ConvLSTM2D(filters=filters, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
-    x = layers.BatchNormalization()(x)
-    # Reshape back to 3D conv format
-    x = layers.Reshape((inputs.shape[1], inputs.shape[2], inputs.shape[3], filters))(x)
-    return x
-
-def decoder_block(inputs, skip_connection, filters):
-    x = layers.Conv3DTranspose(filters=filters, kernel_size=(3, 3, 4), padding="same", activation="relu")(inputs)
-    x = layers.BatchNormalization()(x)
-    skip_resized = layers.Conv3D(filters, (1, 1, 1), padding="same")(skip_connection)
-    x = layers.Concatenate()([x, skip_resized]) 
-    x = layers.ConvLSTM2D(filters=filters, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
-    return x
-
-def build_unet_convlstm(input_shape=(8, 95, 95, 3)):
-    input_tensor = layers.Input(shape=input_shape)
-
-    # Encoder with ConvLSTM
-    skip1 = encoder_block(input_tensor, filters=8)
-    skip1 = convlstm_block(skip1, filters=8)  # Added ConvLSTM
-    
-    skip2 = encoder_block(skip1, filters=16)
-    skip2 = convlstm_block(skip2, filters=16)  # Added ConvLSTM
-
-    # Bottleneck with ConvLSTM
-    x = layers.Conv3D(filters=32, kernel_size=(3, 3, 3), padding="same", activation="relu")(skip2)
-    x = layers.BatchNormalization()(x)
-    x = convlstm_block(x, filters=32)  # Bottleneck ConvLSTM
-
-    # Decoder
-    x = decoder_block(x, skip2, filters=16)
-    x = decoder_block(x, skip1, filters=8)
-
-    # Final Output Layer
-    x = layers.Conv3D(filters=1, kernel_size=(1, 1, 1), activation="relu")(x)
-    x = layers.GlobalAveragePooling3D()(x)
-
-    model = models.Model(inputs=input_tensor, outputs=x)
-    return model
-
-
-import tensorflow as tf
-from tensorflow.keras import layers, models # type: ignore
-
-def RSTNet(input_shape):
-    """
-    Creates the subnet for extracting TC radial structure features using a five-branch CNN design with 2D convolutions.
-
-    Parameters:
-    - input_shape: tuple, shape of the input data (e.g., (95, 95, 3))
-
-    Returns:
-    - model: tf.keras.Model, the radial structure subnet model
-    """
-    
-    input_tensor = layers.Input(shape=input_shape)
-
-    # Divide input data into four quadrants (NW, NE, SW, SE)
-    # Assuming the input shape is (batch_size, height, width, channels)
-    
-    # Quadrant extraction - using slicing to separate quadrants
-    nw_quadrant = input_tensor[:, :input_shape[0]//2, :input_shape[1]//2, :]
-    ne_quadrant = input_tensor[:, :input_shape[0]//2, input_shape[1]//2:, :]
-    sw_quadrant = input_tensor[:, input_shape[0]//2:, :input_shape[1]//2, :]
-    se_quadrant = input_tensor[:, input_shape[0]//2:, input_shape[1]//2:, :]
-
-
-    target_height = max(input_shape[0]//2, input_shape[0] - input_shape[0]//2)  # 48
-    target_width = max(input_shape[1]//2, input_shape[1] - input_shape[1]//2)  # 48
-    
-    # Padding the quadrants to match the target size (48, 48)
-    nw_quadrant = layers.ZeroPadding2D(padding=((0, target_height - nw_quadrant.shape[1]), 
-                                                (0, target_width - nw_quadrant.shape[2])))(nw_quadrant)
-    ne_quadrant = layers.ZeroPadding2D(padding=((0, target_height - ne_quadrant.shape[1]), 
-                                                (0, target_width - ne_quadrant.shape[2])))(ne_quadrant)
-    sw_quadrant = layers.ZeroPadding2D(padding=((0, target_height - sw_quadrant.shape[1]), 
-                                                (0, target_width - sw_quadrant.shape[2])))(sw_quadrant)
-    se_quadrant = layers.ZeroPadding2D(padding=((0, target_height - se_quadrant.shape[1]), 
-                                                (0, target_width - se_quadrant.shape[2])))(se_quadrant)
-
-    print(nw_quadrant.shape)
-    print(ne_quadrant.shape)
-    print(sw_quadrant.shape)
-    print(se_quadrant.shape)
-    # Main branch (processing the entire structure)
-    main_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(input_tensor)
-    y=layers.MaxPool2D()(main_branch)
-
-    y = layers.ZeroPadding2D(padding=((0, target_height - y.shape[1]), 
-                                   (0, target_width - y.shape[2])))(y)
-    # Side branches (processing the individual quadrants)
-    nw_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(nw_quadrant)
-    ne_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(ne_quadrant)
-    sw_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(sw_quadrant)
-    se_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(se_quadrant)
-    
-    # Apply padding to the side branches to match the dimensions of the main branch
-    # nw_branch = layers.UpSampling2D(size=(2, 2), interpolation='nearest')(nw_branch)
-    # ne_branch = layers.UpSampling2D(size=(2, 2), interpolation='nearest')(ne_branch)
-    # sw_branch = layers.UpSampling2D(size=(2, 2), interpolation='nearest')(sw_branch)
-    # se_branch = layers.UpSampling2D(size=(2, 2), interpolation='nearest')(se_branch)
-    
-    # Fusion operations (concatenate the outputs from the main branch and side branches)
-    fusion = layers.concatenate([y, nw_branch, ne_branch, sw_branch, se_branch], axis=-1)
-    
-    # Additional convolution layer to combine the fused features
-    # x = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(fusion)
-    x=layers.Reshape((1, 48, 48, 40))(fusion)
-    x = layers.ConvLSTM2D(filters=16, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
-    x=layers.Reshape((48, 48, 16))(x)
-    x=layers.MaxPool2D(pool_size=(2, 2))(x)
-    # Final dense layer for further processing
-    nw_branch = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(nw_branch)
-    
-    ne_branch = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(ne_branch)
-    sw_branch = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(sw_branch)
-    se_branch = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(se_branch)
-    nw_branch = layers.MaxPool2D(pool_size=(2, 2))(nw_branch)
-    ne_branch = layers.MaxPool2D(pool_size=(2, 2))(ne_branch)
-    sw_branch = layers.MaxPool2D(pool_size=(2, 2))(sw_branch)
-    se_branch = layers.MaxPool2D(pool_size=(2, 2))(se_branch)
-
-    fusion = layers.concatenate([x, nw_branch, ne_branch, sw_branch, se_branch], axis=-1)
-    # x = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(fusion)
-    x=layers.Reshape((1, 24, 24, 80))(fusion)
-    x = layers.ConvLSTM2D(filters=32, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
-    x=layers.Reshape((24, 24, 32))(x)
-    x=layers.MaxPool2D(pool_size=(2, 2))(x)
-    
-    nw_branch = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(nw_branch)
-    
-    ne_branch = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(ne_branch)
-    sw_branch = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(sw_branch)
-    se_branch = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(se_branch)
-    nw_branch = layers.MaxPool2D(pool_size=(2, 2))(nw_branch)
-    ne_branch = layers.MaxPool2D(pool_size=(2, 2))(ne_branch)
-    sw_branch = layers.MaxPool2D(pool_size=(2, 2))(sw_branch)
-    se_branch = layers.MaxPool2D(pool_size=(2, 2))(se_branch)
-
-    fusion = layers.concatenate([x, nw_branch, ne_branch, sw_branch, se_branch], axis=-1)
-    # x = layers.Conv2D(filters=32, kernel_size=(3, 3),  activation='relu')(fusion)
-    x=layers.Reshape((1,12, 12, 160))(fusion)
-    x = layers.ConvLSTM2D(filters=32, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
-    x=layers.Reshape((12, 12, 32))(x)
-    x=layers.Conv2D(filters=32, kernel_size=(3, 3), activation=None)(x)
-    # Create and return the model
-    x=layers.Flatten()(x)
-    model = models.Model(inputs=input_tensor, outputs=x)
-    return model
-
-from tensorflow.keras import layers, models # type: ignore
-
-def build_cnn_model(input_shape=(8, 8, 1)):
-    # Define the input layer
-    input_tensor = layers.Input(shape=input_shape)
-    
-    # Convolutional layer
-    x = layers.Conv2D(64, (3, 3), padding='same')(input_tensor)
-    x = layers.BatchNormalization()(x)
-    x = layers.ReLU()(x)
-    
-    # Flatten layer
-    x = layers.Flatten()(x)
-    
-    # Create the model
-    model = models.Model(inputs=input_tensor, outputs=x)
-    
-    return model
-
-from tensorflow.keras import layers, models, Input # type: ignore
-
-def build_combined_model():
-    # Define input shapes
-    input_shape_3d = (8, 95, 95, 2)
-    input_shape_radial = (95, 95, 8)
-    input_shape_cnn = (8, 8, 1)
-    
-    input_shape_latitude = (8,)
-    input_shape_longitude = (8,)
-    input_shape_other = (9,)
-
-    # Build individual models
-    model_3d = build_unet_convlstm(input_shape=input_shape_3d)
-    model_radial = RSTNet(input_shape=input_shape_radial)
-    model_cnn = build_cnn_model(input_shape=input_shape_cnn)
-
-    # Define new inputs
-    input_latitude = Input(shape=input_shape_latitude ,name="latitude_input")
-    input_longitude = Input(shape=input_shape_longitude, name="longitude_input")
-    input_other = Input(shape=input_shape_other, name="other_input")
-
-    # Flatten the additional inputs
-    flat_latitude = layers.Dense(32,activation='relu')(input_latitude)
-    flat_longitude = layers.Dense(32,activation='relu')(input_longitude)
-    flat_other = layers.Dense(64,activation='relu')(input_other)
-
-    # Combine all outputs
-    combined = layers.concatenate([
-        model_3d.output, 
-        model_radial.output, 
-        model_cnn.output,
-        flat_latitude, 
-        flat_longitude, 
-        flat_other
-    ])
-
-    # Add dense layers for final processing
-    x = layers.Dense(128, activation='relu')(combined)  
-    x = layers.Dense(1, activation=None)(x)
-
-    # Create the final model
-    final_model = models.Model(
-        inputs=[model_3d.input, model_radial.input, model_cnn.input,
-                input_latitude, input_longitude, input_other ],
-        outputs=x
-    )
-
-    return final_model
-
-import h5py
-with h5py.File(r"E:\1MAIN PROJECT\tf_env\final_model.h5", 'r') as f:
-    print(f.attrs.get('keras_version'))
-    print(f.attrs.get('backend'))
-    print("Model layers:", list(f['model_weights'].keys()))
-
-model = build_combined_model()  # Your original model building function
-model.load_weights(r"E:\1MAIN PROJECT\tf_env\final_model.h5")
-
-
-def predict_unetlstm(reduced_images_test,hov_m_test,test_vmax_3d,lat_test,lon_test,int_diff_test):
-    y=model.predict([reduced_images_test,hov_m_test,test_vmax_3d,lat_test,lon_test,int_diff_test ])
+import tensorflow as tf
+from tensorflow.keras import layers, models  # type: ignore
+
+def encoder_block(inputs, filters):
+    x = layers.Conv3D(filters=filters, kernel_size=(3, 3, 4), padding="same", activation="relu")(inputs)
+    x = layers.BatchNormalization()(x)
+    return x
+
+def convlstm_block(inputs, filters):
+    # Reshape to (timesteps, height, width, channels) for ConvLSTM
+    x = layers.Reshape((inputs.shape[1], inputs.shape[2], inputs.shape[3], inputs.shape[4]))(inputs)
+    x = layers.ConvLSTM2D(filters=filters, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
+    x = layers.BatchNormalization()(x)
+    # Reshape back to 3D conv format
+    x = layers.Reshape((inputs.shape[1], inputs.shape[2], inputs.shape[3], filters))(x)
+    return x
+
+def decoder_block(inputs, skip_connection, filters):
+    x = layers.Conv3DTranspose(filters=filters, kernel_size=(3, 3, 4), padding="same", activation="relu")(inputs)
+    x = layers.BatchNormalization()(x)
+    skip_resized = layers.Conv3D(filters, (1, 1, 1), padding="same")(skip_connection)
+    x = layers.Concatenate()([x, skip_resized]) 
+    x = layers.ConvLSTM2D(filters=filters, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
+    return x
+
+def build_unet_convlstm(input_shape=(8, 95, 95, 3)):
+    input_tensor = layers.Input(shape=input_shape)
+
+    # Encoder with ConvLSTM
+    skip1 = encoder_block(input_tensor, filters=8)
+    skip1 = convlstm_block(skip1, filters=8)  # Added ConvLSTM
+    
+    skip2 = encoder_block(skip1, filters=16)
+    skip2 = convlstm_block(skip2, filters=16)  # Added ConvLSTM
+
+    # Bottleneck with ConvLSTM
+    x = layers.Conv3D(filters=32, kernel_size=(3, 3, 3), padding="same", activation="relu")(skip2)
+    x = layers.BatchNormalization()(x)
+    x = convlstm_block(x, filters=32)  # Bottleneck ConvLSTM
+
+    # Decoder
+    x = decoder_block(x, skip2, filters=16)
+    x = decoder_block(x, skip1, filters=8)
+
+    # Final Output Layer
+    x = layers.Conv3D(filters=1, kernel_size=(1, 1, 1), activation="relu")(x)
+    x = layers.GlobalAveragePooling3D()(x)
+
+    model = models.Model(inputs=input_tensor, outputs=x)
+    return model
+
+
+import tensorflow as tf
+from tensorflow.keras import layers, models # type: ignore
+
+def RSTNet(input_shape):
+    """
+    Creates the subnet for extracting TC radial structure features using a five-branch CNN design with 2D convolutions.
+
+    Parameters:
+    - input_shape: tuple, shape of the input data (e.g., (95, 95, 3))
+
+    Returns:
+    - model: tf.keras.Model, the radial structure subnet model
+    """
+    
+    input_tensor = layers.Input(shape=input_shape)
+
+    # Divide input data into four quadrants (NW, NE, SW, SE)
+    # Assuming the input shape is (batch_size, height, width, channels)
+    
+    # Quadrant extraction - using slicing to separate quadrants
+    nw_quadrant = input_tensor[:, :input_shape[0]//2, :input_shape[1]//2, :]
+    ne_quadrant = input_tensor[:, :input_shape[0]//2, input_shape[1]//2:, :]
+    sw_quadrant = input_tensor[:, input_shape[0]//2:, :input_shape[1]//2, :]
+    se_quadrant = input_tensor[:, input_shape[0]//2:, input_shape[1]//2:, :]
+
+
+    target_height = max(input_shape[0]//2, input_shape[0] - input_shape[0]//2)  # 48
+    target_width = max(input_shape[1]//2, input_shape[1] - input_shape[1]//2)  # 48
+    
+    # Padding the quadrants to match the target size (48, 48)
+    nw_quadrant = layers.ZeroPadding2D(padding=((0, target_height - nw_quadrant.shape[1]), 
+                                                (0, target_width - nw_quadrant.shape[2])))(nw_quadrant)
+    ne_quadrant = layers.ZeroPadding2D(padding=((0, target_height - ne_quadrant.shape[1]), 
+                                                (0, target_width - ne_quadrant.shape[2])))(ne_quadrant)
+    sw_quadrant = layers.ZeroPadding2D(padding=((0, target_height - sw_quadrant.shape[1]), 
+                                                (0, target_width - sw_quadrant.shape[2])))(sw_quadrant)
+    se_quadrant = layers.ZeroPadding2D(padding=((0, target_height - se_quadrant.shape[1]), 
+                                                (0, target_width - se_quadrant.shape[2])))(se_quadrant)
+
+    print(nw_quadrant.shape)
+    print(ne_quadrant.shape)
+    print(sw_quadrant.shape)
+    print(se_quadrant.shape)
+    # Main branch (processing the entire structure)
+    main_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(input_tensor)
+    y=layers.MaxPool2D()(main_branch)
+
+    y = layers.ZeroPadding2D(padding=((0, target_height - y.shape[1]), 
+                                   (0, target_width - y.shape[2])))(y)
+    # Side branches (processing the individual quadrants)
+    nw_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(nw_quadrant)
+    ne_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(ne_quadrant)
+    sw_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(sw_quadrant)
+    se_branch = layers.Conv2D(filters=8, kernel_size=(3, 3), padding='same', activation='relu')(se_quadrant)
+    
+    # Apply padding to the side branches to match the dimensions of the main branch
+    # nw_branch = layers.UpSampling2D(size=(2, 2), interpolation='nearest')(nw_branch)
+    # ne_branch = layers.UpSampling2D(size=(2, 2), interpolation='nearest')(ne_branch)
+    # sw_branch = layers.UpSampling2D(size=(2, 2), interpolation='nearest')(sw_branch)
+    # se_branch = layers.UpSampling2D(size=(2, 2), interpolation='nearest')(se_branch)
+    
+    # Fusion operations (concatenate the outputs from the main branch and side branches)
+    fusion = layers.concatenate([y, nw_branch, ne_branch, sw_branch, se_branch], axis=-1)
+    
+    # Additional convolution layer to combine the fused features
+    # x = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(fusion)
+    x=layers.Reshape((1, 48, 48, 40))(fusion)
+    x = layers.ConvLSTM2D(filters=16, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
+    x=layers.Reshape((48, 48, 16))(x)
+    x=layers.MaxPool2D(pool_size=(2, 2))(x)
+    # Final dense layer for further processing
+    nw_branch = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(nw_branch)
+    
+    ne_branch = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(ne_branch)
+    sw_branch = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(sw_branch)
+    se_branch = layers.Conv2D(filters=16, kernel_size=(3, 3), padding='same', activation='relu')(se_branch)
+    nw_branch = layers.MaxPool2D(pool_size=(2, 2))(nw_branch)
+    ne_branch = layers.MaxPool2D(pool_size=(2, 2))(ne_branch)
+    sw_branch = layers.MaxPool2D(pool_size=(2, 2))(sw_branch)
+    se_branch = layers.MaxPool2D(pool_size=(2, 2))(se_branch)
+
+    fusion = layers.concatenate([x, nw_branch, ne_branch, sw_branch, se_branch], axis=-1)
+    # x = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(fusion)
+    x=layers.Reshape((1, 24, 24, 80))(fusion)
+    x = layers.ConvLSTM2D(filters=32, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
+    x=layers.Reshape((24, 24, 32))(x)
+    x=layers.MaxPool2D(pool_size=(2, 2))(x)
+    
+    nw_branch = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(nw_branch)
+    
+    ne_branch = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(ne_branch)
+    sw_branch = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(sw_branch)
+    se_branch = layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')(se_branch)
+    nw_branch = layers.MaxPool2D(pool_size=(2, 2))(nw_branch)
+    ne_branch = layers.MaxPool2D(pool_size=(2, 2))(ne_branch)
+    sw_branch = layers.MaxPool2D(pool_size=(2, 2))(sw_branch)
+    se_branch = layers.MaxPool2D(pool_size=(2, 2))(se_branch)
+
+    fusion = layers.concatenate([x, nw_branch, ne_branch, sw_branch, se_branch], axis=-1)
+    # x = layers.Conv2D(filters=32, kernel_size=(3, 3),  activation='relu')(fusion)
+    x=layers.Reshape((1,12, 12, 160))(fusion)
+    x = layers.ConvLSTM2D(filters=32, kernel_size=(3, 3), padding="same", return_sequences=True)(x)
+    x=layers.Reshape((12, 12, 32))(x)
+    x=layers.Conv2D(filters=32, kernel_size=(3, 3), activation=None)(x)
+    # Create and return the model
+    x=layers.Flatten()(x)
+    model = models.Model(inputs=input_tensor, outputs=x)
+    return model
+
+from tensorflow.keras import layers, models # type: ignore
+
+def build_cnn_model(input_shape=(8, 8, 1)):
+    # Define the input layer
+    input_tensor = layers.Input(shape=input_shape)
+    
+    # Convolutional layer
+    x = layers.Conv2D(64, (3, 3), padding='same')(input_tensor)
+    x = layers.BatchNormalization()(x)
+    x = layers.ReLU()(x)
+    
+    # Flatten layer
+    x = layers.Flatten()(x)
+    
+    # Create the model
+    model = models.Model(inputs=input_tensor, outputs=x)
+    
+    return model
+
+from tensorflow.keras import layers, models, Input # type: ignore
+
+def build_combined_model():
+    # Define input shapes
+    input_shape_3d = (8, 95, 95, 2)
+    input_shape_radial = (95, 95, 8)
+    input_shape_cnn = (8, 8, 1)
+    
+    input_shape_latitude = (8,)
+    input_shape_longitude = (8,)
+    input_shape_other = (9,)
+
+    # Build individual models
+    model_3d = build_unet_convlstm(input_shape=input_shape_3d)
+    model_radial = RSTNet(input_shape=input_shape_radial)
+    model_cnn = build_cnn_model(input_shape=input_shape_cnn)
+
+    # Define new inputs
+    input_latitude = Input(shape=input_shape_latitude ,name="latitude_input")
+    input_longitude = Input(shape=input_shape_longitude, name="longitude_input")
+    input_other = Input(shape=input_shape_other, name="other_input")
+
+    # Flatten the additional inputs
+    flat_latitude = layers.Dense(32,activation='relu')(input_latitude)
+    flat_longitude = layers.Dense(32,activation='relu')(input_longitude)
+    flat_other = layers.Dense(64,activation='relu')(input_other)
+
+    # Combine all outputs
+    combined = layers.concatenate([
+        model_3d.output, 
+        model_radial.output, 
+        model_cnn.output,
+        flat_latitude, 
+        flat_longitude, 
+        flat_other
+    ])
+
+    # Add dense layers for final processing
+    x = layers.Dense(128, activation='relu')(combined)  
+    x = layers.Dense(1, activation=None)(x)
+
+    # Create the final model
+    final_model = models.Model(
+        inputs=[model_3d.input, model_radial.input, model_cnn.input,
+                input_latitude, input_longitude, input_other ],
+        outputs=x
+    )
+
+    return final_model
+
+import h5py
+with h5py.File(r"final_model.h5", 'r') as f:
+    print(f.attrs.get('keras_version'))
+    print(f.attrs.get('backend'))
+    print("Model layers:", list(f['model_weights'].keys()))
+
+model = build_combined_model()  # Your original model building function
+model.load_weights(r"final_model.h5")
+
+
+def predict_unetlstm(reduced_images_test,hov_m_test,test_vmax_3d,lat_test,lon_test,int_diff_test):
+    y=model.predict([reduced_images_test,hov_m_test,test_vmax_3d,lat_test,lon_test,int_diff_test ])
     return y