tf.gather(tf.constant(D['strand_emb'], dtype=tf.float32), x), ), y, )) sequence_encoder = InstanceModels.VariantSequence(6, 4, 2, [16, 16, 8, 8], fusion_dimension=128) mil = RaggedModels.MIL(instance_encoders=[], sample_encoders=[sequence_encoder.model], output_dim=y_label.shape[-1], output_type='other', mil_hidden=[128, 128], mode='none') losses = [Losses.CrossEntropy()] mil.model.compile( loss=losses, metrics=[Metrics.Accuracy(), Metrics.CrossEntropy()], weighted_metrics=[Metrics.Accuracy(), Metrics.CrossEntropy()], optimizer=tf.keras.optimizers.Adam(learning_rate=0.001, )) callbacks = [ tf.keras.callbacks.EarlyStopping(monitor='val_weighted_CE', min_delta=0.001, patience=10, mode='min', restore_best_weights=True) ] mil.model.fit(
strat_dict[(group, event)] for group, event in zip(cancer_labels, y_label[:, 1]) ]) class_counts = dict(zip(*np.unique(y_strat, return_counts=True))) y_weights = np.array([1 / class_counts[_] for _ in y_strat]) y_weights /= np.sum(y_weights) weights = [] callbacks = [ tf.keras.callbacks.EarlyStopping(monitor='val_CE', min_delta=0.0001, patience=50, mode='min', restore_best_weights=True) ] losses = [Losses.CrossEntropy(from_logits=False)] sequence_encoder = InstanceModels.VariantSequence(20, 4, 2, [8, 8, 8, 8]) mil = RaggedModels.MIL(instance_encoders=[sequence_encoder.model], output_dim=2, pooling='sum', mil_hidden=(64, 64, 32, 16), output_type='classification_probability') mil.model.compile( loss=losses, metrics=[Metrics.CrossEntropy(from_logits=False), Metrics.Accuracy()], optimizer=tf.keras.optimizers.Adam(learning_rate=0.001, clipvalue=10000)) initial_weights = mil.model.get_weights() ##stratified K fold for test for idx_train, idx_test in StratifiedKFold(n_splits=9,