Create initial_model_creation.py

initial_model_creation.py

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+import tensorflow as tf
+policy = tf.keras.mixed_precision.Policy("mixed_float16")
+tf.keras.mixed_precision.set_global_policy(policy) 
+from tensorflow import keras
+from tensorflow.keras import layers
+from keras_cv_attention_models import efficientnet
+
+input_shape = (image_frames, None, None, 3)
+image_frames = 60
+image_size = 384
+
+backbone_path = 'efficientnetv2-s-21k-ft1k.h5'
+backbone = efficientnet.EfficientNetV2S(pretrained=backbone_path,dropout=1e-6, num_classes=0, include_preprocessing = True)
+backbone.summary()
+backbone.trainable = False
+
+inputs = keras.Input(shape=input_shape)
+backbone_inputs = keras.Input(shape=(None, None, 3))
+y = backbone(backbone_inputs)
+y = layers.Flatten()(y)
+y = layers.Dense(32, activation="relu")(y)
+y = layers.Dropout(0.1)(y)
+x = layers.TimeDistributed(keras.Model(backbone_inputs, y))(inputs)
+x = layers.Dropout(0.1)(x)
+x = layers.LSTM(128, return_sequences=True)(x)
+x = layers.Dropout(0.1)(x)
+x = layers.LSTM(128, return_sequences=False)(x)
+x = layers.Dropout(0.1)(x)
+x = layers.Dense(128, activation="relu")(x)
+x = layers.Dropout(0.1)(x)
+x = layers.Dense(64, activation="relu")(x)
+x = layers.Dropout(0.1)(x)
+x = layers.Dense(48, activation="relu")(x)
+x = layers.Dropout(0.1)(x)
+x = layers.Dense(32, activation="relu")(x)
+x = layers.Dropout(0.1)(x)
+outputs = layers.Dense(9, activation="relu")(x)
+model = keras.Model(inputs, outputs)
+model.compile(
+    optimizer=keras.optimizers.Adam(1e-3),
+    loss="mean_squared_error",
+    metrics=["mean_squared_error", "mean_absolute_error"]
+)