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| import tensorflow as tf | |
| from tensorflow.keras import layers, models # type: ignore | |
| import numpy as np | |
| # Define the ConvGRU2DLayer (same as before) | |
| def build_3d_conv_lstm_model(input_shape=(8, 95, 95, 2), batch_size=16): | |
| """ | |
| Builds a 3D ConvLSTM model with Conv3D layers and MaxPooling3D layers. | |
| Parameters: | |
| - input_shape: Shape of the input tensor (time_steps, height, width, channels). | |
| - batch_size: Batch size for the model. | |
| Returns: | |
| - model: The compiled Keras model. | |
| """ | |
| # Input tensor | |
| input_tensor = layers.Input(shape=input_shape) | |
| # First ConvLSTM2D block with Conv3D | |
| x = layers.ConvLSTM2D(filters=32, kernel_size=(3, 3), padding='same', return_sequences=True)(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=(4, 3, 3), padding='same')(x) | |
| # Second ConvLSTM2D block with Conv3D | |
| x = layers.ConvLSTM2D(filters=64, kernel_size=(3, 3), padding='same', return_sequences=True)(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=(4, 3, 3), padding='same')(x) | |
| # Third ConvLSTM2D block with Conv3D | |
| x = layers.ConvLSTM2D(filters=128, kernel_size=(3, 3), padding='same', return_sequences=True)(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 the output before passing to the fully connected layers | |
| x = layers.Flatten()(x) | |
| # Create the final 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_3d_conv_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"lstm3dcnn-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"lstm3dcnn-model.h5") | |
| def predict_lstm(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 |