diff --git a/testdata/dnn/tensorflow/generate_tf2_models.py b/testdata/dnn/tensorflow/generate_tf2_models.py
index 0834ec85d77ca537a9ad84c0b4438e6e2f98c693..61928554b6bcbe0d6ae8be92b696930afa74e7e3 100644
--- a/testdata/dnn/tensorflow/generate_tf2_models.py
+++ b/testdata/dnn/tensorflow/generate_tf2_models.py
@@ -76,9 +76,9 @@ model = tf.keras.models.Sequential([
 save(model, 'tf2_dense', flatten_input=tf.TensorSpec(shape=[None, 1, 2, 3], dtype=tf.float32))
 ################################################################################
 model = tf.keras.models.Sequential([
-  tf.keras.layers.PReLU(input_shape=(1, 2, 3)),
+  tf.keras.layers.PReLU(input_shape=(1, 4, 6), alpha_initializer='random_normal'),
 ])
-save(model, 'tf2_prelu', p_re_lu_input=tf.TensorSpec(shape=[None, 1, 2, 3], dtype=tf.float32))
+save(model, 'tf2_prelu', p_re_lu_input=tf.TensorSpec(shape=[None, 1, 4, 6], dtype=tf.float32))
 ################################################################################
 model = tf.keras.models.Sequential([
   tf.keras.layers.AveragePooling2D(input_shape=(4, 6, 3), pool_size=(2, 2)),
diff --git a/testdata/dnn/tensorflow/tf2_prelu_in.npy b/testdata/dnn/tensorflow/tf2_prelu_in.npy
index 3f341818db5901df0f1aa9ecb4a82b30f6e46186..2896b22ac2dcc696f9ded43d984f386a43f3ad81 100644
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diff --git a/testdata/dnn/tensorflow/tf2_prelu_net.pb b/testdata/dnn/tensorflow/tf2_prelu_net.pb
index 80dc11783799b66c1d7c5ba0ff08c7204c6b05ba..ac7c21f3a04f5ad38001cb9656f9157c60ae2cd7 100644
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diff --git a/testdata/dnn/tensorflow/tf2_prelu_out.npy b/testdata/dnn/tensorflow/tf2_prelu_out.npy
index 3f341818db5901df0f1aa9ecb4a82b30f6e46186..a8fa513f53e0eece994b23f2c32f6efdecc32395 100644
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