diff --git a/testdata/dnn/tensorflow/eltwise_add_vec_in.npy b/testdata/dnn/tensorflow/eltwise_add_vec_in.npy
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index 000000000..a5959d955
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diff --git a/testdata/dnn/tensorflow/eltwise_add_vec_net.pb b/testdata/dnn/tensorflow/eltwise_add_vec_net.pb
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index 000000000..ace25ece1
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diff --git a/testdata/dnn/tensorflow/eltwise_add_vec_out.npy b/testdata/dnn/tensorflow/eltwise_add_vec_out.npy
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index 000000000..1ce1c6b82
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diff --git a/testdata/dnn/tensorflow/eltwise_mul_vec_in.npy b/testdata/dnn/tensorflow/eltwise_mul_vec_in.npy
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index 000000000..3094cee03
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diff --git a/testdata/dnn/tensorflow/eltwise_mul_vec_net.pb b/testdata/dnn/tensorflow/eltwise_mul_vec_net.pb
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index 000000000..13833e021
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diff --git a/testdata/dnn/tensorflow/eltwise_mul_vec_out.npy b/testdata/dnn/tensorflow/eltwise_mul_vec_out.npy
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index 000000000..10737972d
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diff --git a/testdata/dnn/tensorflow/generate_tf_models.py b/testdata/dnn/tensorflow/generate_tf_models.py
index ef0be0990..1d933d006 100644
--- a/testdata/dnn/tensorflow/generate_tf_models.py
+++ b/testdata/dnn/tensorflow/generate_tf_models.py
@@ -416,6 +416,16 @@ def my_dropout(x):
 out = tf.reshape(conv2, [1, 2, 3, 6], 'reshaped')
 save(inp, out, 'reshape_nchw')
 ################################################################################
+inp = tf.placeholder(tf.float32, [1, 5, 5, 3], 'input')
+out = tf.keras.layers.MaxPool2D((2, 2), 4, "SAME", name='pooling')(inp)
+reshape = tf.reshape(out, [-1, 1, 1, 12], 'reshaped')
+conv_filter = tf.get_variable('filter', [1, 1, 12, 4],
+                              initializer=tf.truncated_normal_initializer(),
+                              dtype=tf.float32)
+conv = tf.nn.conv2d(input=reshape, filters=conv_filter, strides=[1, 1, 1, 1],
+                    padding='SAME', name='conv2d')
+save(inp, conv, 'reshape_conv')
+################################################################################
 inp = tf.placeholder(tf.float32, [1, 6, 5, 3], 'input')
 conv = tf.layers.conv2d(inputs=inp, filters=3, kernel_size=[1, 1],
                         activation=tf.nn.relu,
@@ -786,6 +796,18 @@ def pad_depth(x, desired_channels):
 sub = conv - inp
 save(inp, sub, 'eltwise_sub')
 ################################################################################
+inp = tf.placeholder(tf.float32, [1, 5, 5, 10], 'input')
+out1 = tf.keras.layers.MaxPool2D((2, 2), 5, "SAME", name="pooling")(inp)
+out2 = tf.keras.layers.ReLU(name="relu")(inp)
+final_out = tf.math.add(out1, out2, name='tf_sum')
+save(inp, final_out, 'eltwise_add_vec')
+################################################################################
+inp = tf.placeholder(tf.float32, [1, 4, 4, 3], 'input')
+out1 = tf.keras.layers.MaxPool2D((2, 2), 4, "SAME", name="pooling")(inp)
+out2 = tf.keras.layers.ReLU(name="relu")(inp)
+final_out = tf.keras.layers.Multiply(name='tf_mul')([out1, out2])
+save(inp, final_out, 'eltwise_mul_vec')
+################################################################################
 inp = tf.placeholder(tf.float32, [None, 2, 3, 4], 'input')
 conv = tf.layers.conv2d(inp, filters=3, kernel_size=[1, 1])
 softmax = tf.contrib.slim.softmax(conv)
diff --git a/testdata/dnn/tensorflow/reshape_conv_in.npy b/testdata/dnn/tensorflow/reshape_conv_in.npy
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index 000000000..7a0feda5b
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diff --git a/testdata/dnn/tensorflow/reshape_conv_net.pb b/testdata/dnn/tensorflow/reshape_conv_net.pb
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index 000000000..cb2bfc9ce
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diff --git a/testdata/dnn/tensorflow/reshape_conv_out.npy b/testdata/dnn/tensorflow/reshape_conv_out.npy
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index 000000000..25a767efa
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