Update spaio_temp.py

spaio_temp.py

@@ -1,327 +1,327 @@
-import tensorflow as tf
-from tensorflow.keras import layers, models # type: ignore
-import numpy as np
-
-
-class SpatiotemporalLSTMCell(layers.Layer):
-    """
-    SpatiotemporalLSTMCell: A custom LSTM cell that captures both spatial and temporal dependencies.
-    It extends the traditional LSTM by adding a memory state (m_t) that focuses on spatial correlations.
-    """
-    def __init__(self, filters, kernel_size, **kwargs):
-        super().__init__(**kwargs)
-        self.filters = filters  # Number of output filters in the convolution
-        self.kernel_size = kernel_size  # Size of the convolutional kernel
-        
-        # Convolutional components for standard LSTM operations
-        self.conv_xg = layers.Conv2D(filters, kernel_size, padding="same", activation="tanh")    # For cell input
-        self.conv_xi = layers.Conv2D(filters, kernel_size, padding="same", activation="sigmoid") # For input gate
-        self.conv_xf = layers.Conv2D(filters, kernel_size, padding="same", activation="sigmoid") # For forget gate
-        self.conv_xo = layers.Conv2D(filters, kernel_size, padding="same", activation="sigmoid") # For output gate
-        
-        # Convolutional components for spatiotemporal memory operations
-        self.conv_xg_st = layers.Conv2D(filters, kernel_size, padding="same", activation="tanh")    # For ST cell input
-        self.conv_xi_st = layers.Conv2D(filters, kernel_size, padding="same", activation="sigmoid") # For ST input gate
-        self.conv_xf_st = layers.Conv2D(filters, kernel_size, padding="same", activation="sigmoid") # For ST forget gate
-        
-        # Fusion layer to combine the cell state and spatiotemporal memory
-        self.conv_fusion = layers.Conv2D(filters, (1, 1), padding="same")  # 1x1 conv for dimensionality reduction
-    
-    def call(self, inputs, states):
-        """
-        Forward pass of the spatiotemporal LSTM cell.
-        
-        Args:
-            inputs: Input tensor of shape [batch_size, height, width, channels]
-            states: List of previous states [h_t-1, c_t-1, m_t-1]
-                   h_t-1: previous hidden state
-                   c_t-1: previous cell state
-                   m_t-1: previous spatiotemporal memory
-        """
-        prev_h, prev_c, prev_m = states
-        
-        # Standard LSTM operations
-        g_t = self.conv_xg(inputs) + self.conv_xg(prev_h)  # Cell input activation
-        i_t = self.conv_xi(inputs) + self.conv_xi(prev_h)  # Input gate
-        f_t = self.conv_xf(inputs) + self.conv_xf(prev_h)  # Forget gate
-        o_t = self.conv_xo(inputs) + self.conv_xo(prev_h)  # Output gate
-        
-        # Cell state update - bug detected: should use prev_c instead of self.conv_xo(prev_h)
-        c_t = tf.sigmoid(f_t) * self.conv_xo(prev_h) + tf.sigmoid(i_t) * tf.tanh(g_t)
-        
-        # Spatiotemporal memory operations
-        g_t_st = self.conv_xg_st(inputs) + self.conv_xg_st(prev_m)  # ST cell input
-        i_t_st = self.conv_xi_st(inputs) + self.conv_xi_st(prev_m)  # ST input gate
-        f_t_st = self.conv_xf_st(inputs) + self.conv_xf_st(prev_m)  # ST forget gate
-        
-        # Spatiotemporal memory update - bug detected: should use prev_m directly instead of self.conv_xf_st(prev_m)
-        m_t = tf.sigmoid(f_t_st) * self.conv_xf_st(prev_m) + tf.sigmoid(i_t_st) * tf.tanh(g_t_st)
-        
-        # Hidden state update by fusing cell state and spatiotemporal memory
-        h_t = tf.sigmoid(o_t) * tf.tanh(self.conv_fusion(tf.concat([c_t, m_t], axis=-1)))
-        
-        return h_t, [h_t, c_t, m_t]  # Return the hidden state and all updated states
-
-class SpatiotemporalLSTM(layers.Layer):
-    """
-    SpatiotemporalLSTM: Custom layer that applies the SpatiotemporalLSTMCell to a sequence of inputs.
-    This processes 3D data with spatial and temporal dimensions.
-    """
-    def __init__(self, filters, kernel_size, **kwargs):
-        super().__init__(**kwargs)
-        self.cell = SpatiotemporalLSTMCell(filters, kernel_size)
-    
-    def call(self, inputs):
-        """
-        Forward pass of the SpatiotemporalLSTM layer.
-        
-        Args:
-            inputs: Input tensor of shape [batch_size, time_steps, height, width, channels]
-        """
-        batch_size = tf.shape(inputs)[0]
-        time_steps = inputs.shape[1]
-        height = inputs.shape[2]
-        width = inputs.shape[3]
-        channels = inputs.shape[4]
-        
-        # Initialize states with zeros
-        h_t = tf.zeros((batch_size, height, width, channels))  # Hidden state
-        c_t = tf.zeros((batch_size, height, width, channels))  # Cell state
-        m_t = tf.zeros((batch_size, height, width, channels))  # Spatiotemporal memory
-        
-        outputs = []
-        # Process sequence step by step
-        for t in range(time_steps):
-            # Apply the cell to the current time step and previous states
-            h_t, [h_t, c_t, m_t] = self.cell(inputs[:, t], [h_t[:,:,:,:inputs.shape[4]], 
-                                                          c_t[:,:,:,:inputs.shape[4]], 
-                                                          m_t[:,:,:,:inputs.shape[4]]])
-            outputs.append(h_t)
-        
-        # Stack outputs along time dimension
-        return tf.stack(outputs, axis=1)
-
-def build_st_lstm_model(input_shape=(8, 95, 95, 2)):
-    """
-    Build a complete spatiotemporal LSTM model for sequence processing of spatial data.
-    
-    Args:
-        input_shape: Tuple of (time_steps, height, width, channels)
-    
-    Returns:
-        A Keras model with spatiotemporal LSTM layers
-    """
-    # Create input layer with fixed batch size
-    input_tensor = layers.Input(shape=input_shape, batch_size=16)
-    
-    # First spatiotemporal LSTM block
-    st_lstm_layer = SpatiotemporalLSTM(filters=32, kernel_size=(3, 3))
-    x = st_lstm_layer(input_tensor)
-    x = layers.Conv3D(filters=32, kernel_size=(3, 3, 3), padding='same', activation='relu')(x)
-    x = layers.MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), padding='same')(x)
-    
-    # Second spatiotemporal LSTM block
-    st_lstm_layer = SpatiotemporalLSTM(filters=64, kernel_size=(3, 3))
-    x = st_lstm_layer(x)
-    x = layers.Conv3D(filters=64, kernel_size=(3, 3, 3), padding='same', activation='relu')(x)
-    x = layers.MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), padding='same')(x)
-    
-    # Third spatiotemporal LSTM block
-    st_lstm_layer = SpatiotemporalLSTM(filters=128, kernel_size=(3, 3))
-    x = st_lstm_layer(x)
-    x = layers.Conv3D(filters=128, kernel_size=(3, 3, 3), padding='same', activation='relu')(x)
-    x = layers.MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), padding='same')(x)
-    
-    # Flatten and prepare for output layers (not included in this model)
-    x = layers.Flatten()(x)
-    
-    # Create and return the model
-    model = models.Model(inputs=input_tensor, outputs=x)
-    return model
-
-def radial_structure_subnet(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.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.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.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
-
-# Define input shape (batch_size, height, width, channels)
-# input_shape = (95, 95, 8)  # Example input shape (95x95 spatial resolution, 3 channels)
-
-# # Build the model
-# model = radial_structure_subnet(input_shape)
-
-# # Model summary
-# model.summary()
-
-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_st_lstm_model(input_shape=input_shape_3d)
-    model_radial = radial_structure_subnet(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\spatio_tempral_LSTM.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\spatio_tempral_LSTM.h5")
-
-
-def predict_stlstm(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
+import numpy as np
+
+
+class SpatiotemporalLSTMCell(layers.Layer):
+    """
+    SpatiotemporalLSTMCell: A custom LSTM cell that captures both spatial and temporal dependencies.
+    It extends the traditional LSTM by adding a memory state (m_t) that focuses on spatial correlations.
+    """
+    def __init__(self, filters, kernel_size, **kwargs):
+        super().__init__(**kwargs)
+        self.filters = filters  # Number of output filters in the convolution
+        self.kernel_size = kernel_size  # Size of the convolutional kernel
+        
+        # Convolutional components for standard LSTM operations
+        self.conv_xg = layers.Conv2D(filters, kernel_size, padding="same", activation="tanh")    # For cell input
+        self.conv_xi = layers.Conv2D(filters, kernel_size, padding="same", activation="sigmoid") # For input gate
+        self.conv_xf = layers.Conv2D(filters, kernel_size, padding="same", activation="sigmoid") # For forget gate
+        self.conv_xo = layers.Conv2D(filters, kernel_size, padding="same", activation="sigmoid") # For output gate
+        
+        # Convolutional components for spatiotemporal memory operations
+        self.conv_xg_st = layers.Conv2D(filters, kernel_size, padding="same", activation="tanh")    # For ST cell input
+        self.conv_xi_st = layers.Conv2D(filters, kernel_size, padding="same", activation="sigmoid") # For ST input gate
+        self.conv_xf_st = layers.Conv2D(filters, kernel_size, padding="same", activation="sigmoid") # For ST forget gate
+        
+        # Fusion layer to combine the cell state and spatiotemporal memory
+        self.conv_fusion = layers.Conv2D(filters, (1, 1), padding="same")  # 1x1 conv for dimensionality reduction
+    
+    def call(self, inputs, states):
+        """
+        Forward pass of the spatiotemporal LSTM cell.
+        
+        Args:
+            inputs: Input tensor of shape [batch_size, height, width, channels]
+            states: List of previous states [h_t-1, c_t-1, m_t-1]
+                   h_t-1: previous hidden state
+                   c_t-1: previous cell state
+                   m_t-1: previous spatiotemporal memory
+        """
+        prev_h, prev_c, prev_m = states
+        
+        # Standard LSTM operations
+        g_t = self.conv_xg(inputs) + self.conv_xg(prev_h)  # Cell input activation
+        i_t = self.conv_xi(inputs) + self.conv_xi(prev_h)  # Input gate
+        f_t = self.conv_xf(inputs) + self.conv_xf(prev_h)  # Forget gate
+        o_t = self.conv_xo(inputs) + self.conv_xo(prev_h)  # Output gate
+        
+        # Cell state update - bug detected: should use prev_c instead of self.conv_xo(prev_h)
+        c_t = tf.sigmoid(f_t) * self.conv_xo(prev_h) + tf.sigmoid(i_t) * tf.tanh(g_t)
+        
+        # Spatiotemporal memory operations
+        g_t_st = self.conv_xg_st(inputs) + self.conv_xg_st(prev_m)  # ST cell input
+        i_t_st = self.conv_xi_st(inputs) + self.conv_xi_st(prev_m)  # ST input gate
+        f_t_st = self.conv_xf_st(inputs) + self.conv_xf_st(prev_m)  # ST forget gate
+        
+        # Spatiotemporal memory update - bug detected: should use prev_m directly instead of self.conv_xf_st(prev_m)
+        m_t = tf.sigmoid(f_t_st) * self.conv_xf_st(prev_m) + tf.sigmoid(i_t_st) * tf.tanh(g_t_st)
+        
+        # Hidden state update by fusing cell state and spatiotemporal memory
+        h_t = tf.sigmoid(o_t) * tf.tanh(self.conv_fusion(tf.concat([c_t, m_t], axis=-1)))
+        
+        return h_t, [h_t, c_t, m_t]  # Return the hidden state and all updated states
+
+class SpatiotemporalLSTM(layers.Layer):
+    """
+    SpatiotemporalLSTM: Custom layer that applies the SpatiotemporalLSTMCell to a sequence of inputs.
+    This processes 3D data with spatial and temporal dimensions.
+    """
+    def __init__(self, filters, kernel_size, **kwargs):
+        super().__init__(**kwargs)
+        self.cell = SpatiotemporalLSTMCell(filters, kernel_size)
+    
+    def call(self, inputs):
+        """
+        Forward pass of the SpatiotemporalLSTM layer.
+        
+        Args:
+            inputs: Input tensor of shape [batch_size, time_steps, height, width, channels]
+        """
+        batch_size = tf.shape(inputs)[0]
+        time_steps = inputs.shape[1]
+        height = inputs.shape[2]
+        width = inputs.shape[3]
+        channels = inputs.shape[4]
+        
+        # Initialize states with zeros
+        h_t = tf.zeros((batch_size, height, width, channels))  # Hidden state
+        c_t = tf.zeros((batch_size, height, width, channels))  # Cell state
+        m_t = tf.zeros((batch_size, height, width, channels))  # Spatiotemporal memory
+        
+        outputs = []
+        # Process sequence step by step
+        for t in range(time_steps):
+            # Apply the cell to the current time step and previous states
+            h_t, [h_t, c_t, m_t] = self.cell(inputs[:, t], [h_t[:,:,:,:inputs.shape[4]], 
+                                                          c_t[:,:,:,:inputs.shape[4]], 
+                                                          m_t[:,:,:,:inputs.shape[4]]])
+            outputs.append(h_t)
+        
+        # Stack outputs along time dimension
+        return tf.stack(outputs, axis=1)
+
+def build_st_lstm_model(input_shape=(8, 95, 95, 2)):
+    """
+    Build a complete spatiotemporal LSTM model for sequence processing of spatial data.
+    
+    Args:
+        input_shape: Tuple of (time_steps, height, width, channels)
+    
+    Returns:
+        A Keras model with spatiotemporal LSTM layers
+    """
+    # Create input layer with fixed batch size
+    input_tensor = layers.Input(shape=input_shape, batch_size=16)
+    
+    # First spatiotemporal LSTM block
+    st_lstm_layer = SpatiotemporalLSTM(filters=32, kernel_size=(3, 3))
+    x = st_lstm_layer(input_tensor)
+    x = layers.Conv3D(filters=32, kernel_size=(3, 3, 3), padding='same', activation='relu')(x)
+    x = layers.MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), padding='same')(x)
+    
+    # Second spatiotemporal LSTM block
+    st_lstm_layer = SpatiotemporalLSTM(filters=64, kernel_size=(3, 3))
+    x = st_lstm_layer(x)
+    x = layers.Conv3D(filters=64, kernel_size=(3, 3, 3), padding='same', activation='relu')(x)
+    x = layers.MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), padding='same')(x)
+    
+    # Third spatiotemporal LSTM block
+    st_lstm_layer = SpatiotemporalLSTM(filters=128, kernel_size=(3, 3))
+    x = st_lstm_layer(x)
+    x = layers.Conv3D(filters=128, kernel_size=(3, 3, 3), padding='same', activation='relu')(x)
+    x = layers.MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2), padding='same')(x)
+    
+    # Flatten and prepare for output layers (not included in this model)
+    x = layers.Flatten()(x)
+    
+    # Create and return the model
+    model = models.Model(inputs=input_tensor, outputs=x)
+    return model
+
+def radial_structure_subnet(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.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.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.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
+
+# Define input shape (batch_size, height, width, channels)
+# input_shape = (95, 95, 8)  # Example input shape (95x95 spatial resolution, 3 channels)
+
+# # Build the model
+# model = radial_structure_subnet(input_shape)
+
+# # Model summary
+# model.summary()
+
+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_st_lstm_model(input_shape=input_shape_3d)
+    model_radial = radial_structure_subnet(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"spatio_tempral_LSTM.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"spatio_tempral_LSTM.h5")
+
+
+def predict_stlstm(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