TBD===Training the model======Pruning the model=== <pre>conv2d_1/kernel:0 -- Param: 864 -- Zeros: 00.00%conv2d_1/bias:0 -- Param: 32 -- Zeros: 00.00%conv2d_2/kernel:0 -- Param: 9216 -- Zeros: 00.00%conv2d_2/bias:0 -- Param: 32 -- Zeros: 00.00%conv2d_3/kernel:0 -- Param: 18432 -- Zeros: 00.00%conv2d_3/bias:0 -- Param: 64 -- Zeros: 00.00%conv2d_4/kernel:0 -- Param: 73728 -- Zeros: 00.00%conv2d_4/bias:0 -- Param: 128 -- Zeros: 00.00%dense_1/kernel:0 -- Param: 4718592 -- Zeros: 00.00%dense_1/bias:0 -- Param: 256 -- Zeros: 00.39%predictions/kernel:0 -- Param: 1536 -- Zeros: 00.00%predictions/bias:0 -- Param: 6 -- Zeros: 00.00%</pre> <pre>Size of gzipped loaded model: 17801431.00 bytes</pre> <pre>Test set1/1 [==============================] - 0s 214ms/step - loss: 1.3166 - acc: 0.7083</pre>  <pre>conv2d_1/kernel:0 -- Param: 864 -- Zeros: 00.00%conv2d_1/bias:0 -- Param: 32 -- Zeros: 00.00%conv2d_2/kernel:0 -- Param: 9216 -- Zeros: 00.00%conv2d_2/bias:0 -- Param: 32 -- Zeros: 00.00%conv2d_3/kernel:0 -- Param: 18432 -- Zeros: 00.00%conv2d_3/bias:0 -- Param: 64 -- Zeros: 00.00%conv2d_4/kernel:0 -- Param: 73728 -- Zeros: 00.00%conv2d_4/bias:0 -- Param: 128 -- Zeros: 00.00%dense_1/kernel:0 -- Param: 4718592 -- Zeros: 80.00%dense_1/bias:0 -- Param: 256 -- Zeros: 00.00%predictions/kernel:0 -- Param: 1536 -- Zeros: 80.01%predictions/bias:0 -- Param: 6 -- Zeros: 00.00%</pre> <pre>Size of gzipped loaded model: 5795289.00 bytes</pre> <pre>Test set1/1 [==============================] - 0s 29ms/step - loss: 1.4578 - acc: 0.6667</pre> ===Freezing the computational graph=== '''Baseline model'''<pre>INFO:tensorflow:Froze 12 variables.I1002 09:08:49.716494 140705992206144 graph_util_impl.py:334] Froze 12 variables.INFO:tensorflow:Converted 12 variables to const ops.I1002 09:08:49.776397 140705992206144 graph_util_impl.py:394] Converted 12 variables to const ops.</pre> ===Transform the computational graph=== '''Applied transformations'''<pre>transformations_list = ['remove_nodes(op=Identity, op=CheckNumerics)', 'merge_duplicate_nodes', 'strip_unused_nodes', 'fold_constants(ignore_errors=true)', 'fold_batch_norms']</pre> '''Baseline model'''<pre>describe : frozen_graph.pbinput feature nodes : ['images_in']unused nodes : []output nodes : ['predictions/kernel', 'predictions/bias', 'predictions/MatMul/ReadVariableOp', 'predictions/MatMul', 'predictions/BiasAdd/ReadVariableOp', 'predictions/BiasAdd', 'predictions/Softmax']quantization nodes : []constant count : 16variable count : 0identity count : 13total nodes : 56</pre> <pre>Op: Placeholder -- Name: images_in Op: Const -- Name: conv2d_1/kernel Op: Const -- Name: conv2d_1/bias Op: Identity -- Name: conv2d_1/Conv2D/ReadVariableOpOp: Conv2D -- Name: conv2d_1/Conv2D Op: Identity -- Name: conv2d_1/BiasAdd/ReadVariableOpOp: BiasAdd -- Name: conv2d_1/BiasAdd Op: Relu -- Name: conv2d_1/Relu Op: MaxPool -- Name: maxpool_1/MaxPool Op: Const -- Name: conv2d_2/kernel Op: Const -- Name: conv2d_2/bias Op: Identity -- Name: conv2d_2/Conv2D/ReadVariableOpOp: Conv2D -- Name: conv2d_2/Conv2D Op: Identity -- Name: conv2d_2/BiasAdd/ReadVariableOpOp: BiasAdd -- Name: conv2d_2/BiasAdd Op: Relu -- Name: conv2d_2/Relu Op: MaxPool -- Name: maxpool_2/MaxPool Op: Const -- Name: conv2d_3/kernel Op: Const -- Name: conv2d_3/bias Op: Identity -- Name: conv2d_3/Conv2D/ReadVariableOpOp: Conv2D -- Name: conv2d_3/Conv2D Op: Identity -- Name: conv2d_3/BiasAdd/ReadVariableOpOp: BiasAdd -- Name: conv2d_3/BiasAdd Op: Relu -- Name: conv2d_3/Relu Op: MaxPool -- Name: maxpool_3/MaxPool Op: Const -- Name: conv2d_4/kernel Op: Const -- Name: conv2d_4/bias Op: Identity -- Name: conv2d_4/Conv2D/ReadVariableOpOp: Conv2D -- Name: conv2d_4/Conv2D Op: Identity -- Name: conv2d_4/BiasAdd/ReadVariableOpOp: BiasAdd -- Name: conv2d_4/BiasAdd Op: Relu -- Name: conv2d_4/Relu Op: MaxPool -- Name: maxpool_4/MaxPool Op: Shape -- Name: flatten/Shape Op: Const -- Name: flatten/strided_slice/stack Op: Const -- Name: flatten/strided_slice/stack_1 Op: Const -- Name: flatten/strided_slice/stack_2 Op: StridedSlice -- Name: flatten/strided_slice Op: Const -- Name: flatten/Reshape/shape/1 Op: Pack -- Name: flatten/Reshape/shape Op: Reshape -- Name: flatten/Reshape Op: Const -- Name: dense_1/kernel Op: Const -- Name: dense_1/bias Op: Identity -- Name: dense_1/MatMul/ReadVariableOp Op: MatMul -- Name: dense_1/MatMul Op: Identity -- Name: dense_1/BiasAdd/ReadVariableOpOp: BiasAdd -- Name: dense_1/BiasAdd Op: Relu -- Name: dense_1/Relu Op: Identity -- Name: dropout_1/Identity Op: Const -- Name: predictions/kernel Op: Const -- Name: predictions/bias Op: Identity -- Name: predictions/MatMul/ReadVariableOpOp: MatMul -- Name: predictions/MatMul Op: Identity -- Name: predictions/BiasAdd/ReadVariableOpOp: BiasAdd -- Name: predictions/BiasAdd Op: Softmax -- Name: predictions/Softmax</pre> <pre>describe : baseline_transf_graph.pbinput feature nodes : ['images_in']unused nodes : []output nodes : ['predictions/MatMul', 'predictions/kernel', 'predictions/bias', 'predictions/Softmax', 'predictions/BiasAdd']quantization nodes : []constant count : 15variable count : 0identity count : 0total nodes : 42</pre> <pre>Op: Conv2D -- Name: conv2d_1/Conv2D Op: BiasAdd -- Name: conv2d_2/BiasAdd Op: Relu -- Name: conv2d_4/Relu Op: Conv2D -- Name: conv2d_3/Conv2D Op: Const -- Name: conv2d_2/kernel Op: MaxPool -- Name: maxpool_4/MaxPool Op: Const -- Name: conv2d_1/kernel Op: Const -- Name: conv2d_3/kernel Op: Placeholder -- Name: images_in Op: Pack -- Name: flatten/Reshape/shape Op: Const -- Name: conv2d_3/bias Op: Const -- Name: conv2d_4/kernel Op: Reshape -- Name: flatten/Reshape Op: Shape -- Name: flatten/Shape Op: Conv2D -- Name: conv2d_4/Conv2D Op: Const -- Name: conv2d_2/bias Op: MaxPool -- Name: maxpool_2/MaxPool Op: Relu -- Name: conv2d_1/Relu Op: MatMul -- Name: predictions/MatMul Op: BiasAdd -- Name: dense_1/BiasAdd Op: MaxPool -- Name: maxpool_1/MaxPool Op: Const -- Name: flatten/strided_slice/stack Op: Const -- Name: dense_1/kernel Op: BiasAdd -- Name: conv2d_1/BiasAdd Op: Const -- Name: flatten/Reshape/shape/1 Op: Const -- Name: predictions/kernel Op: BiasAdd -- Name: conv2d_4/BiasAdd Op: Const -- Name: conv2d_1/bias Op: Relu -- Name: conv2d_2/Relu Op: Const -- Name: flatten/strided_slice/stack_1 Op: Const -- Name: dense_1/bias Op: Const -- Name: predictions/bias Op: Conv2D -- Name: conv2d_2/Conv2D Op: MaxPool -- Name: maxpool_3/MaxPool Op: Const -- Name: conv2d_4/bias Op: Relu -- Name: dense_1/Relu Op: Relu -- Name: conv2d_3/Relu Op: Softmax -- Name: predictions/Softmax Op: BiasAdd -- Name: conv2d_3/BiasAdd Op: MatMul -- Name: dense_1/MatMul Op: StridedSlice -- Name: flatten/strided_slice Op: BiasAdd -- Name: predictions/BiasAdd</pre> <>Graph accuracy with test dataset: 0.7083</pre> <pre>Graph accuracy with test dataset: 0.6667</pre> ===Quantize the computational graph=== '''Baseline model'''<pre>graph accuracy with test dataset: 0.7083</pre> '''Pruned model'''<pre>graph accuracy with test dataset: 0.7083</pre> ===Compiling the model===  '''Baseline model'''<pre>Kernel topology "custom_cnn_kernel_graph.jpg" for network "custom_cnn"kernel list info for network "custom_cnn" Kernel ID : Name 0 : custom_cnn_0 1 : custom_cnn_1  Kernel Name : custom_cnn_0-------------------------------------------------------------------------------- Kernel Type : DPUKernel Code Size : 0.02MB Param Size : 4.60MB Workload MACs : 498.21MOPS IO Memory Space : 0.52MB Mean Value : 0, 0, 0, Total Tensor Count : 7 Boundary Input Tensor(s) (H*W*C) images_in:0(0) : 224*224*3  Boundary Output Tensor(s) (H*W*C) predictions_MatMul:0(0) : 1*1*6  Total Node Count : 6 Input Node(s) (H*W*C) conv2d_1_Conv2D(0) : 224*224*3  Output Node(s) (H*W*C) predictions_MatMul(0) : 1*1*6     Kernel Name : custom_cnn_1-------------------------------------------------------------------------------- Kernel Type : CPUKernel Boundary Input Tensor(s) (H*W*C) predictions_Softmax:0(0) : 1*1*6  Boundary Output Tensor(s) (H*W*C) predictions_Softmax:0(0) : 1*1*6  Input Node(s) (H*W*C) predictions_Softmax : 1* questo è un elenco puntato 1*6  Output Node(s) scrivo nel blocco dell(H*W*C) predictions_Softmax : 1*1*6</pre> ''elenco puntato'Pruned model'''<pre>Kernel topology "pruned_custom_cnn_kernel_graph.jpg" for network "pruned_custom_cnn"kernel list info for network "pruned_custom_cnn" Kernel ID : Name 0 : pruned_custom_cnn_0 1 : pruned_custom_cnn_1  Kernel Name : pruned_custom_cnn_0-------------------------------------------------------------------------------- Kernel Type : DPUKernel Code Size : 0.02MB Param Size : 4.60MB Workload MACs : 498.21MOPS IO Memory Space : 0.52MB Mean Value : 0, 0, 0, Total Tensor Count : 7 Boundary Input Tensor(s) (H*W*C) images_in:0(0) : 224*224*3  Boundary Output Tensor(s) (H*W*C) predictions_MatMul:0(0) : 1*1*6  Total Node Count : 6 Input Node(s) (H* questo è un elenco puntato 2W*C) conv2d_1_Conv2D(0) : 224*224* questo è un elenco puntato 3  Output Node(s) (H*W*C) predictions_MatMul(0) : 1*1*6     Kernel Name : pruned_custom_cnn_1-------------------------------------------------------------------------------- Kernel Type : CPUKernel Boundary Input Tensor(s) (H* questo è un elenco puntato 4W*C) predictions_Softmax:0(0) : 1*1*6  Boundary Output Tensor(s) (H*W*C) predictions_Softmax:0(0) : 1*1*6  Input Node(s) (H*W*C) predictions_Softmax : 1*1*6  Output Node(s) (H*W*C) predictions_Softmax : 1*1*6</pre>