Neural network specified with tf.layers inserting undesired reshape operations

This simple neural network defined in python:

input_layer = tf.placeholder(tf.float32, shape=[1, 1, 8000, 1], name='input_layer')

# Convolutional Layer
conv = tf.layers.conv2d(
        inputs=input_layer,
        filters=32,
        kernel_size=[1,11],
        padding="same",
        strides=1,
        activation=tf.nn.relu)

# Output layer
logits = tf.layers.dense(inputs=conv, units=5, name='logit')

Results the following graph topology:

0 input_layer Placeholder
1 conv2d/kernel/Initializer/random_uniform/shape Const
2 conv2d/kernel/Initializer/random_uniform/min Const
3 conv2d/kernel/Initializer/random_uniform/max Const
4 conv2d/kernel/Initializer/random_uniform/RandomUniform RandomUniform
└─── Input0 ─ conv2d/kernel/Initializer/random_uniform/shape
5 conv2d/kernel/Initializer/random_uniform/sub Sub
└─── Input0 ─ conv2d/kernel/Initializer/random_uniform/max
└─── Input1 ─ conv2d/kernel/Initializer/random_uniform/min
6 conv2d/kernel/Initializer/random_uniform/mul Mul
└─── Input0 ─ conv2d/kernel/Initializer/random_uniform/RandomUniform
└─── Input1 ─ conv2d/kernel/Initializer/random_uniform/sub
7 conv2d/kernel/Initializer/random_uniform Add
└─── Input0 ─ conv2d/kernel/Initializer/random_uniform/mul
└─── Input1 ─ conv2d/kernel/Initializer/random_uniform/min
8 conv2d/kernel VariableV2
9 conv2d/kernel/Assign Assign
└─── Input0 ─ conv2d/kernel
└─── Input1 ─ conv2d/kernel/Initializer/random_uniform
10 conv2d/kernel/read Identity
└─── Input0 ─ conv2d/kernel
11 conv2d/bias/Initializer/zeros Const
12 conv2d/bias VariableV2
13 conv2d/bias/Assign Assign
└─── Input0 ─ conv2d/bias
└─── Input1 ─ conv2d/bias/Initializer/zeros
14 conv2d/bias/read Identity
└─── Input0 ─ conv2d/bias
15 conv2d/convolution/Shape Const
16 conv2d/convolution/dilation_rate Const
17 conv2d/convolution Conv2D
└─── Input0 ─ input_layer
└─── Input1 ─ conv2d/kernel/read
18 conv2d/BiasAdd BiasAdd
└─── Input0 ─ conv2d/convolution
└─── Input1 ─ conv2d/bias/read
19 conv2d/Relu Relu
└─── Input0 ─ conv2d/BiasAdd
20 logit/kernel/Initializer/random_uniform/shape Const
21 logit/kernel/Initializer/random_uniform/min Const
22 logit/kernel/Initializer/random_uniform/max Const
23 logit/kernel/Initializer/random_uniform/RandomUniform RandomUniform
└─── Input0 ─ logit/kernel/Initializer/random_uniform/shape
24 logit/kernel/Initializer/random_uniform/sub Sub
└─── Input0 ─ logit/kernel/Initializer/random_uniform/max
└─── Input1 ─ logit/kernel/Initializer/random_uniform/min
25 logit/kernel/Initializer/random_uniform/mul Mul
└─── Input0 ─ logit/kernel/Initializer/random_uniform/RandomUniform
└─── Input1 ─ logit/kernel/Initializer/random_uniform/sub
26 logit/kernel/Initializer/random_uniform Add
└─── Input0 ─ logit/kernel/Initializer/random_uniform/mul
└─── Input1 ─ logit/kernel/Initializer/random_uniform/min
27 logit/kernel VariableV2
28 logit/kernel/Assign Assign
└─── Input0 ─ logit/kernel
└─── Input1 ─ logit/kernel/Initializer/random_uniform
29 logit/kernel/read Identity
└─── Input0 ─ logit/kernel
30 logit/bias/Initializer/zeros Const
31 logit/bias VariableV2
32 logit/bias/Assign Assign
└─── Input0 ─ logit/bias
└─── Input1 ─ logit/bias/Initializer/zeros
33 logit/bias/read Identity
└─── Input0 ─ logit/bias
34 logit/Tensordot/transpose/perm Const
35 logit/Tensordot/transpose Transpose
└─── Input0 ─ conv2d/Relu
└─── Input1 ─ logit/Tensordot/transpose/perm
36 logit/Tensordot/Reshape/shape Const
37 logit/Tensordot/Reshape Reshape
└─── Input0 ─ logit/Tensordot/transpose
└─── Input1 ─ logit/Tensordot/Reshape/shape
38 logit/Tensordot/transpose_1/perm Const
39 logit/Tensordot/transpose_1 Transpose
└─── Input0 ─ logit/kernel/read
└─── Input1 ─ logit/Tensordot/transpose_1/perm
40 logit/Tensordot/Reshape_1/shape Const
41 logit/Tensordot/Reshape_1 Reshape
└─── Input0 ─ logit/Tensordot/transpose_1
└─── Input1 ─ logit/Tensordot/Reshape_1/shape
42 logit/Tensordot/MatMul MatMul
└─── Input0 ─ logit/Tensordot/Reshape
└─── Input1 ─ logit/Tensordot/Reshape_1
43 logit/Tensordot/shape Const
44 logit/Tensordot Reshape
└─── Input0 ─ logit/Tensordot/MatMul
└─── Input1 ─ logit/Tensordot/shape
45 logit/BiasAdd BiasAdd
└─── Input0 ─ logit/Tensordot
└─── Input1 ─ logit/bias/read

What is the purpose of the reshape operations between nodes 36 and 44? I am working with the Snapdragon Neural Processing Engine (SNPE) which does not permit reshape operations. Is there any way to express this model without the reshape ops?

All of these reshape ops are added by tf.tensordot, which is used by tf.layers.dense for high-dimensional inputs (in your case 4D). From its documentation:

Note: if the inputs tensor has a rank greater than 2, then it is flattened prior to the initial matrix multiply by kernel.

The link to the source code.

If reshaping is undesired in your environment, try to define the weights and biases manually and apply the dot product via tf.matmul.