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__init__.py

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+#    This file was created by
+#    MATLAB Deep Learning Toolbox Converter for TensorFlow Models.
+#    13-Aug-2025 21:15:39
+
+import TextStoryAI.model
+import os
+
+def load_model(load_weights=True, debug=False):
+    m = model.create_model()
+    if load_weights:
+        loadWeights(m, debug=debug)
+    return m
+
+## Utility functions:
+
+import tensorflow as tf
+import h5py
+
+def loadWeights(model, filename=os.path.join(__package__, "weights.h5"), debug=False):
+    with h5py.File(filename, 'r') as f:
+        # Every layer is an h5 group. Ignore non-groups (such as /0)
+        for g in f:
+            if isinstance(f[g], h5py.Group):
+                group = f[g]
+                layerName = group.attrs['Name']
+                numVars = int(group.attrs['NumVars'])
+                if debug:
+                    print("layerName:", layerName)
+                    print("    numVars:", numVars)
+                # Find the layer index from its namevar
+                layerIdx = layerNum(model, layerName)
+                layer = model.layers[layerIdx]
+                if debug:
+                    print("    layerIdx=", layerIdx)
+                # Every weight is an h5 dataset in the layer group. Read the weights 
+                # into a list in the correct order
+                weightList = [0]*numVars
+                for d in group:
+                    dataset = group[d]
+                    varName = dataset.attrs['Name']
+                    shp     = intList(dataset.attrs['Shape'])
+                    weightNum = int(dataset.attrs['WeightNum'])
+                    # Read the weight and put it into the right position in the list
+                    if debug:
+                        print("    varName:", varName)
+                        print("        shp:", shp)
+                        print("        weightNum:", weightNum)
+                    weightList[weightNum] = tf.constant(dataset[()], shape=shp)
+                # Assign the weights into the layer
+                for w in range(numVars):
+                    if debug:
+                        print("Copying variable of shape:")
+                        print(weightList[w].shape)
+                    layer.variables[w].assign(weightList[w])
+                    if debug:
+                        print("Assignment successful.")
+                        print("Set variable value:")
+                        print(layer.variables[w])
+                # Finalize layer state
+                if hasattr(layer, 'finalize_state'):
+                    layer.finalize_state()
+
+def layerNum(model, layerName):
+    # Returns the index to the layer
+    layers = model.layers
+    for i in range(len(layers)):
+        if layerName==layers[i].name:
+            return i
+    print("")
+    print("WEIGHT LOADING FAILED. MODEL DOES NOT CONTAIN LAYER WITH NAME: ", layerName)
+    print("")
+    return -1
+
+def intList(myList): 
+    # Converts a list of numbers into a list of ints.
+    return list(map(int, myList))
+

model.py

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+#    This file was created by
+#    MATLAB Deep Learning Toolbox Converter for TensorFlow Models.
+#    13-Aug-2025 21:15:39
+
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+def create_model():
+    input = keras.Input(shape=(None,1894))
+    input_lstm_input = input
+    lstm = layers.LSTM(128, name='lstm_', activation='tanh', recurrent_activation='sigmoid', return_sequences=False, return_state=False)(input_lstm_input)
+    fc = layers.Dense(1894, name="fc_")(lstm)
+    softmax = layers.Softmax()(fc)
+
+    model = keras.Model(inputs=[input], outputs=[softmax])
+    return model

weights.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1afcb1e8a2d8f2f5523975906cd00c3766561ed9b1dc4e0324b779341e34ce6a
+size 5168200