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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 |