Options
"Convolution Neural Network (CNN) with Keras Model"
Contributor I<?xml version="1.0" encoding="UTF-8"?><process version="9.0.003">
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<operator activated="true" class="retrieve" compatibility="9.0.003" expanded="true" height="68" name="Retrieve Test CORN_FLOUR" width="90" x="45" y="544">
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<operator activated="false" class="concurrency:cross_validation" compatibility="9.0.003" expanded="true" height="145" name="Cross Validation Keras" origin="GENERATED_TRAINING" width="90" x="715" y="391">
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<parameter key="optimizer" value="Adam"/>
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<parameter key="size_2d" value="2.2"/>
<parameter key="size_3d" value="2.2"/>
<parameter key="data_format" value="None"/>
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<operator activated="true" class="keras:conv_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Convolutional Layer (8)" width="90" x="715" y="136">
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<parameter key="size_3d" value="2.2"/>
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<operator activated="true" class="keras:pooling_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Pooling Layer (8)" width="90" x="849" y="136">
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<operator activated="true" class="keras:conv_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Convolutional Layer (9)" width="90" x="715" y="238">
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<parameter key="dilation_rate_2d" value="1.1"/>
<parameter key="dilation_rate_3d" value="1.1"/>
<parameter key="activation_function" value="'relu'"/>
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<operator activated="true" class="keras:pooling_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Pooling Layer (9)" width="90" x="849" y="238">
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<parameter key="cropping_1d" value="1.1"/>
<parameter key="size_2d" value="2.2"/>
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<parameter key="data_format" value="None"/>
<parameter key="dilation_rate_2d" value="1.1"/>
<parameter key="dilation_rate_3d" value="1.1"/>
<parameter key="activation_function" value="'relu'"/>
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<operator activated="true" class="keras:pooling_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Pooling Layer (10)" width="90" x="849" y="340">
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<parameter key="strides_3d" value="1.1"/>
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<parameter key="padding_3d" value="(1, 1, 1)"/>
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<parameter key="size_2d" value="2.2"/>
<parameter key="size_3d" value="2.2"/>
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<parameter key="dilation_rate_2d" value="1.1"/>
<parameter key="dilation_rate_3d" value="1.1"/>
<parameter key="activation_function" value="'relu'"/>
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<parameter key="strides_1d" value="1"/>
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<parameter key="strides_3d" value="2.2"/>
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<parameter key="dims" value="1.1"/>
<parameter key="repetition_factor" value="2"/>
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<operator activated="true" class="keras:core_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Core Layer (9)" width="90" x="1318" y="136">
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<operator activated="true" class="keras:core_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Core Layer (10)" width="90" x="1318" y="238">
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<description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="1114" y="134">Il layer FLATTEN &#232; usato per Input a pi&#249; dimensioni e trasforma un layer a pi&#249; dimensioni in una</description>
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<list key="class_weights"/>
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<operator activated="true" class="retrieve" compatibility="9.0.003" expanded="true" height="68" name="Retrieve Test CORN_FLOUR (2)" width="90" x="45" y="136">
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<parameter key="repository_entry" value="Test RICE_STARCH"/>
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<operator activated="true" class="retrieve" compatibility="9.0.003" expanded="true" height="68" name="Retrieve Training RICE_STARCH (2)" width="90" x="45" y="238">
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<operator activated="true" class="retrieve" compatibility="9.0.003" expanded="true" height="68" name="Retrieve Training SUGAR (2)" width="90" x="45" y="340">
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<connect from_op="Retrieve Training RICE_STARCH (2)" from_port="output" to_op="Union (21)" to_port="example set 1"/>
<connect from_op="Retrieve Training SUGAR (2)" from_port="output" to_op="Union (21)" to_port="example set 2"/>
<connect from_op="Union (21)" from_port="union" to_op="Union (20)" to_port="example set 2"/>
<connect from_op="Union (20)" from_port="union" to_port="out 1"/>
<portSpacing port="source_in 1" spacing="0"/>
<portSpacing port="sink_out 1" spacing="0"/>
<portSpacing port="sink_out 2" spacing="0"/>
</process>
</operator>
<operator activated="false" class="subprocess" compatibility="9.0.003" expanded="true" height="82" name="TrainingData 3000Examples" width="90" x="45" y="34">
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="9.0.003" expanded="true" height="68" name="Retrieve Training_Rice_Starch" width="90" x="45" y="238">
<parameter key="repository_entry" value="../Training_Rice_Starch"/>
</operator>
<operator activated="true" class="retrieve" compatibility="9.0.003" expanded="true" height="68" name="Retrieve Training_Baby_Milk" width="90" x="45" y="136">
<parameter key="repository_entry" value="../Training_Baby_Milk"/>
</operator>
<operator activated="true" class="retrieve" compatibility="9.0.003" expanded="true" height="68" name="Retrieve Training_Cocoa" width="90" x="45" y="34">
<parameter key="repository_entry" value="../Training_Cocoa"/>
</operator>
<operator activated="true" class="union" compatibility="9.0.003" expanded="true" height="82" name="Union (8)" width="90" x="246" y="85"/>
<operator activated="true" class="union" compatibility="9.0.003" expanded="true" height="82" name="Union (10)" width="90" x="447" y="187"/>
<connect from_op="Retrieve Training_Rice_Starch" from_port="output" to_op="Union (10)" to_port="example set 2"/>
<connect from_op="Retrieve Training_Baby_Milk" from_port="output" to_op="Union (8)" to_port="example set 2"/>
<connect from_op="Retrieve Training_Cocoa" from_port="output" to_op="Union (8)" to_port="example set 1"/>
<connect from_op="Union (8)" from_port="union" to_op="Union (10)" to_port="example set 1"/>
<connect from_op="Union (10)" from_port="union" to_port="out 1"/>
<portSpacing port="source_in 1" spacing="0"/>
<portSpacing port="sink_out 1" spacing="0"/>
<portSpacing port="sink_out 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="subprocess" compatibility="9.0.003" expanded="true" height="82" name="Normalize TrainData" width="90" x="313" y="34">
<process expanded="true">
<operator activated="true" class="multiply" compatibility="9.0.003" expanded="true" height="103" name="Multiply" width="90" x="45" y="136"/>
<operator activated="true" class="select_attributes" compatibility="9.0.003" expanded="true" height="82" name="Select Attributes (2)" width="90" x="246" y="187">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="KP"/>
</operator>
<operator activated="true" class="generate_id" compatibility="9.0.003" expanded="true" height="82" name="Generate ID (3)" width="90" x="782" y="187"/>
<operator activated="true" class="select_attributes" compatibility="9.0.003" expanded="true" height="82" name="Select Attributes" width="90" x="246" y="34">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="|Z|Y|X|W|V|U|T|S|R|Q|P|O|N|M|L|KO|KN|KM|KL|KK|KJ|KI|KH|KG|KF|KE|KD|KC|KB|KA|K|JZ|JY|JX|JW|JV|JU|JT|JS|JR|JQ|JP|JO|JN|JM|JL|JK|JJ|JI|JH|JG|JF|JE|JD|JC|JB|JA|J|IZ|IY|IX|IW|IV|IU|IT|IS|IR|IQ|IP|IO|IN|IM|IL|IK|IJ|II|IH|IG|IF|IE|ID|IC|IB|IA|I|HZ|HY|HX|HW|HV|HU|HT|HS|HR|HQ|HP|HO|HN|HM|HL|HK|HJ|HI|HH|HG|HF|HE|HD|HC|HB|HA|H|GZ|GY|GX|GW|GV|GU|GT|GS|GR|GQ|GP|GO|GN|GM|GL|GK|GJ|GI|GH|GG|GF|GE|GD|GC|GB|GA|G|FZ|FY|FX|FW|FV|FU|FT|FS|FR|FQ|FP|FO|FN|FM|FL|FK|FJ|FI|FH|FG|FF|FE|FD|FC|FB|FA|F|EZ|EY|EX|EW|EV|EU|ET|ES|ER|EQ|EP|EO|EN|EM|EL|EK|EJ|EI|EH|EG|EF|EE|ED|EC|EB|EA|E|DZ|DY|DX|DW|DV|DU|DT|DS|DR|DQ|DP|DO|DN|DM|DL|DK|DJ|DI|DH|DG|DF|DE|DD|DC|DB|DA|D|CZ|CY|CX|CW|CV|CU|CT|CS|CR|CQ|CP|CO|CN|CM|CL|CK|CJ|CI|CH|CG|CF|CE|CD|CC|CB|CA|C|BZ|BY|BX|BW|BV|BU|BT|BS|BR|BQ|BP|BO|BN|BM|BL|BK|BJ|BI|BH|BG|BF|BE|BD|BC|BB|BA|B|AZ|AY|AX|AW|AV|AU|AT|AS|AR|AQ|AP|AO|AN|AM|AL|AK|AJ|AI|AH|AG|AF|AE|AD|AC|AB|AA|A"/>
</operator>
<operator activated="true" class="normalize" compatibility="9.0.003" expanded="true" height="103" name="Range Trasformation (2)" origin="GENERATED_SAMPLE" width="90" x="380" y="34">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attribute" value="KP"/>
<parameter key="attributes" value="|Z|Y|X|W|V|U|T|S|R|Q|P|O|N|M|L|KO|KN|KM|KL|KK|KJ|KI|KH|KG|KF|KE|KD|KC|KB|KA|K|JZ|JY|JX|JW|JV|JU|JT|JS|JR|JQ|JP|JO|JN|JM|JL|JK|JJ|JI|JH|JG|JF|JE|JD|JC|JB|JA|J|IZ|IY|IX|IW|IV|IU|IT|IS|IR|IQ|IP|IO|IN|IM|IL|IK|IJ|II|IH|IG|IF|IE|ID|IC|IB|IA|I|HZ|HY|HX|HW|HV|HU|HT|HS|HR|HQ|HP|HO|HN|HM|HL|HK|HJ|HI|HH|HG|HF|HE|HD|HC|HB|HA|H|GZ|GY|GX|GW|GV|GU|GT|GS|GR|GQ|GP|GO|GN|GM|GL|GK|GJ|GI|GH|GG|GF|GE|GD|GC|GB|GA|G|FZ|FY|FX|FW|FV|FU|FT|FS|FR|FQ|FP|FO|FN|FM|FL|FK|FJ|FI|FH|FG|FF|FE|FD|FC|FB|FA|F|EZ|EY|EX|EW|EV|EU|ET|ES|ER|EQ|EP|EO|EN|EM|EL|EK|EJ|EI|EH|EG|EF|EE|ED|EC|EB|EA|E|DZ|DY|DX|DW|DV|DU|DT|DS|DR|DQ|DP|DO|DN|DM|DL|DK|DJ|DI|DH|DG|DF|DE|DD|DC|DB|DA|D|CZ|CY|CX|CW|CV|CU|CT|CS|CR|CQ|CP|CO|CN|CM|CL|CK|CJ|CI|CH|CG|CF|CE|CD|CC|CB|CA|C|BZ|BY|BX|BW|BV|BU|BT|BS|BR|BQ|BP|BO|BN|BM|BL|BK|BJ|BI|BH|BG|BF|BE|BD|BC|BB|BA|B|AZ|AY|AX|AW|AV|AU|AT|AS|AR|AQ|AP|AO|AN|AM|AL|AK|AJ|AI|AH|AG|AF|AE|AD|AC|AB|AA|A"/>
<parameter key="method" value="range transformation"/>
</operator>
<operator activated="true" class="select_attributes" compatibility="9.0.003" expanded="true" height="82" name="Select Attributes (13)" width="90" x="514" y="34">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="KP"/>
<parameter key="invert_selection" value="true"/>
</operator>
<operator activated="true" class="series:windowing" compatibility="7.4.000" expanded="true" height="82" name="Windowing (2)" origin="GENERATED_SAMPLE" width="90" x="648" y="34">
<parameter key="window_size" value="1"/>
<parameter key="label_attribute" value="Close"/>
<parameter key="stop_on_too_small_dataset" value="false"/>
</operator>
<operator activated="true" class="generate_id" compatibility="9.0.003" expanded="true" height="82" name="Generate ID (4)" width="90" x="782" y="34"/>
<operator activated="true" class="concurrency:join" compatibility="9.0.003" expanded="true" height="82" name="Join PCA_Corr" width="90" x="983" y="85">
<parameter key="use_id_attribute_as_key" value="true"/>
<list key="key_attributes"/>
</operator>
<connect from_port="in 1" to_op="Multiply" to_port="input"/>
<connect from_op="Multiply" from_port="output 1" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Multiply" from_port="output 2" to_op="Select Attributes (2)" to_port="example set input"/>
<connect from_op="Select Attributes (2)" from_port="example set output" to_op="Generate ID (3)" to_port="example set input"/>
<connect from_op="Generate ID (3)" from_port="example set output" to_op="Join PCA_Corr" to_port="right"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Range Trasformation (2)" to_port="example set input"/>
<connect from_op="Range Trasformation (2)" from_port="example set output" to_op="Select Attributes (13)" to_port="example set input"/>
<connect from_op="Select Attributes (13)" from_port="example set output" to_op="Windowing (2)" to_port="example set input"/>
<connect from_op="Windowing (2)" from_port="example set output" to_op="Generate ID (4)" to_port="example set input"/>
<connect from_op="Generate ID (4)" from_port="example set output" to_op="Join PCA_Corr" to_port="left"/>
<connect from_op="Join PCA_Corr" from_port="join" to_port="out 1"/>
<portSpacing port="source_in 1" spacing="0"/>
<portSpacing port="source_in 2" spacing="0"/>
<portSpacing port="sink_out 1" spacing="0"/>
<portSpacing port="sink_out 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="set_role" compatibility="9.0.003" expanded="true" height="82" name="Set Role Train" origin="GENERATED_SAMPLE" width="90" x="447" y="34">
<parameter key="attribute_name" value="KP"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="false" class="subprocess" compatibility="9.0.003" expanded="true" height="82" name="TestData 1000examples" width="90" x="45" y="187">
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="9.0.003" expanded="true" height="68" name="Retrieve Test_Baby_Milk" width="90" x="45" y="187">
<parameter key="repository_entry" value="../Test_Baby_Milk"/>
</operator>
<operator activated="true" class="retrieve" compatibility="9.0.003" expanded="true" height="68" name="Retrieve Test_Rice_Starch" width="90" x="45" y="289">
<parameter key="repository_entry" value="../Test_Rice_Starch"/>
</operator>
<operator activated="true" class="retrieve" compatibility="9.0.003" expanded="true" height="68" name="Retrieve Test_Cocoa" width="90" x="45" y="85">
<parameter key="repository_entry" value="../Test_Cocoa"/>
</operator>
<operator activated="true" class="union" compatibility="9.0.003" expanded="true" height="82" name="Union (11)" width="90" x="313" y="85"/>
<operator activated="true" class="union" compatibility="9.0.003" expanded="true" height="82" name="Union (13)" width="90" x="514" y="136"/>
<connect from_op="Retrieve Test_Baby_Milk" from_port="output" to_op="Union (11)" to_port="example set 2"/>
<connect from_op="Retrieve Test_Rice_Starch" from_port="output" to_op="Union (13)" to_port="example set 2"/>
<connect from_op="Retrieve Test_Cocoa" from_port="output" to_op="Union (11)" to_port="example set 1"/>
<connect from_op="Union (11)" from_port="union" to_op="Union (13)" to_port="example set 1"/>
<connect from_op="Union (13)" from_port="union" to_port="out 1"/>
<portSpacing port="source_in 1" spacing="0"/>
<portSpacing port="sink_out 1" spacing="0"/>
<portSpacing port="sink_out 2" spacing="0"/>
</process>
</operator>
<operator activated="false" class="multiply" compatibility="9.0.003" expanded="true" height="68" name="Multiply (4)" width="90" x="581" y="85"/>
<operator activated="true" class="keras:sequential" compatibility="1.0.003" expanded="true" height="166" name="Keras Model" width="90" x="715" y="34">
<parameter key="input shape" value="(1,301)"/>
<parameter key="loss" value="categorical_crossentropy"/>
<parameter key="optimizer" value="Adam"/>
<parameter key="learning rate" value="0.001"/>
<enumeration key="metric"/>
<parameter key="epochs" value="512"/>
<parameter key="batch size" value="64"/>
<enumeration key="callbacks">
<parameter key="callbacks" value="TensorBoard(log_dir='./logs', histogram_freq=0, write_graph=True, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None)"/>
</enumeration>
<process expanded="true">
<operator activated="false" class="keras:conv_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Convolutional Layer (4)" width="90" x="715" y="34">
<parameter key="filters" value="64"/>
<parameter key="kernel_size_2d" value="1.1"/>
<parameter key="kernel_size_3d" value="1.1"/>
<parameter key="strides_2d" value="1.1"/>
<parameter key="strides_3d" value="1.1"/>
<parameter key="padding_1d" value="1."/>
<parameter key="padding_2d" value="(1, 1)"/>
<parameter key="padding_3d" value="(1, 1, 1)"/>
<parameter key="cropping_1d" value="1.1"/>
<parameter key="size_2d" value="2.2"/>
<parameter key="size_3d" value="2.2"/>
<parameter key="data_format" value="None"/>
<parameter key="dilation_rate_2d" value="1.1"/>
<parameter key="dilation_rate_3d" value="1.1"/>
<parameter key="activation_function" value="'relu'"/>
</operator>
<operator activated="false" class="keras:pooling_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Pooling Layer (4)" width="90" x="849" y="34">
<parameter key="pool_size_1d" value="5"/>
<parameter key="pool_size_2d" value="2.2"/>
<parameter key="pool_size_3d" value="2.2"/>
<parameter key="strides_1d" value="1"/>
<parameter key="strides_2d" value="2.2"/>
<parameter key="strides_3d" value="2.2"/>
</operator>
<operator activated="false" class="keras:conv_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Convolutional Layer (3)" width="90" x="715" y="136">
<parameter key="filters" value="64"/>
<parameter key="kernel_size_2d" value="1.1"/>
<parameter key="kernel_size_3d" value="1.1"/>
<parameter key="strides_2d" value="1.1"/>
<parameter key="strides_3d" value="1.1"/>
<parameter key="padding_1d" value="1."/>
<parameter key="padding_2d" value="(1, 1)"/>
<parameter key="padding_3d" value="(1, 1, 1)"/>
<parameter key="cropping_1d" value="1.1"/>
<parameter key="size_2d" value="2.2"/>
<parameter key="size_3d" value="2.2"/>
<parameter key="data_format" value="None"/>
<parameter key="dilation_rate_2d" value="1.1"/>
<parameter key="dilation_rate_3d" value="1.1"/>
<parameter key="activation_function" value="'relu'"/>
</operator>
<operator activated="false" class="keras:pooling_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Pooling Layer (3)" width="90" x="849" y="136">
<parameter key="pool_size_1d" value="4"/>
<parameter key="pool_size_2d" value="2.2"/>
<parameter key="pool_size_3d" value="2.2"/>
<parameter key="strides_1d" value="1"/>
<parameter key="strides_2d" value="2.2"/>
<parameter key="strides_3d" value="2.2"/>
</operator>
<operator activated="false" class="keras:conv_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Convolutional Layer (5)" width="90" x="715" y="238">
<parameter key="filters" value="64"/>
<parameter key="kernel_size_2d" value="1.1"/>
<parameter key="kernel_size_3d" value="1.1"/>
<parameter key="strides_2d" value="1.1"/>
<parameter key="strides_3d" value="1.1"/>
<parameter key="padding_1d" value="1."/>
<parameter key="padding_2d" value="(1, 1)"/>
<parameter key="padding_3d" value="(1, 1, 1)"/>
<parameter key="cropping_1d" value="1.1"/>
<parameter key="size_2d" value="2.2"/>
<parameter key="size_3d" value="2.2"/>
<parameter key="data_format" value="None"/>
<parameter key="dilation_rate_2d" value="1.1"/>
<parameter key="dilation_rate_3d" value="1.1"/>
<parameter key="activation_function" value="'relu'"/>
</operator>
<operator activated="false" class="keras:pooling_layer" compatibility="1.0.003" expanded="true" height="68" name="Add Pooling Layer (5)" width="90" x="849" y="238">
<parameter key="pool_size_1d" value="4"/>
<parameter key="pool_size_2d" value="2.2"/>
<parameter key="pool_size_3d" value="2.2"/>
<parameter key="strides_1d" value="1"/>
<parameter key="strides_2d" value="2.2"/>
<parameter key="strides_3d" value="2.2"/>
</operator>
<operator activated="false" class="keras:conv_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Convolutional Layer (6)" width="90" x="715" y="340">
<parameter key="filters" value="64"/>
<parameter key="kernel_size_2d" value="1.1"/>
<parameter key="kernel_size_3d" value="1.1"/>
<parameter key="strides_2d" value="1.1"/>
<parameter key="strides_3d" value="1.1"/>
<parameter key="padding_1d" value="1."/>
<parameter key="padding_2d" value="(1, 1)"/>
<parameter key="padding_3d" value="(1, 1, 1)"/>
<parameter key="cropping_1d" value="1.1"/>
<parameter key="size_2d" value="2.2"/>
<parameter key="size_3d" value="2.2"/>
<parameter key="data_format" value="None"/>
<parameter key="dilation_rate_2d" value="1.1"/>
<parameter key="dilation_rate_3d" value="1.1"/>
<parameter key="activation_function" value="'relu'"/>
</operator>
<operator activated="false" class="keras:pooling_layer" compatibility="1.0.003" expanded="true" height="68" name="Add Pooling Layer (6)" width="90" x="849" y="340">
<parameter key="pool_size_1d" value="1"/>
<parameter key="pool_size_2d" value="2.2"/>
<parameter key="pool_size_3d" value="2.2"/>
<parameter key="strides_1d" value="1"/>
<parameter key="strides_2d" value="2.2"/>
<parameter key="strides_3d" value="2.2"/>
</operator>
<operator activated="true" class="keras:conv_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Convolutional Layer (2)" width="90" x="715" y="442">
<parameter key="filters" value="64"/>
<parameter key="kernel_size_2d" value="1.1"/>
<parameter key="kernel_size_3d" value="1.1"/>
<parameter key="strides_2d" value="1.1"/>
<parameter key="strides_3d" value="1.1"/>
<parameter key="padding_1d" value="1."/>
<parameter key="padding_2d" value="(1, 1)"/>
<parameter key="padding_3d" value="(1, 1, 1)"/>
<parameter key="cropping_1d" value="1.1"/>
<parameter key="size_2d" value="2.2"/>
<parameter key="size_3d" value="2.2"/>
<parameter key="data_format" value="None"/>
<parameter key="dilation_rate_2d" value="1.1"/>
<parameter key="dilation_rate_3d" value="1.1"/>
<parameter key="activation_function" value="'relu'"/>
</operator>
<operator activated="true" class="keras:pooling_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Pooling Layer (2)" width="90" x="849" y="442">
<parameter key="pool_size_1d" value="1"/>
<parameter key="pool_size_2d" value="2.2"/>
<parameter key="pool_size_3d" value="2.2"/>
<parameter key="strides_1d" value="1"/>
<parameter key="strides_2d" value="2.2"/>
<parameter key="strides_3d" value="2.2"/>
</operator>
<operator activated="true" class="keras:core_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Core Layer" width="90" x="1050" y="34">
<parameter key="layer_type" value="Dropout"/>
<parameter key="rate" value="0.25"/>
<parameter key="target_shape" value="1.1"/>
<parameter key="dims" value="1.1"/>
<parameter key="repetition_factor" value="2"/>
</operator>
<operator activated="true" class="keras:core_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Core Layer (3)" width="90" x="1184" y="34">
<parameter key="layer_type" value="Flatten"/>
<parameter key="target_shape" value="1.1"/>
<parameter key="dims" value="1.1"/>
<parameter key="repetition_factor" value="2"/>
</operator>
<operator activated="true" class="keras:core_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Core Layer (5)" width="90" x="1318" y="34">
<parameter key="no_units" value="40"/>
<parameter key="activation_function" value="'sigmoid'"/>
<parameter key="target_shape" value="1.1"/>
<parameter key="dims" value="1.1"/>
<parameter key="repetition_factor" value="2"/>
</operator>
<operator activated="true" class="keras:core_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Core Layer (11)" width="90" x="1318" y="136">
<parameter key="no_units" value="20"/>
<parameter key="activation_function" value="'sigmoid'"/>
<parameter key="target_shape" value="1.1"/>
<parameter key="dims" value="1.1"/>
<parameter key="repetition_factor" value="2"/>
</operator>
<operator activated="true" class="keras:core_layer" compatibility="1.0.003" expanded="true" height="82" name="Add Core Layer (7)" width="90" x="1318" y="238">
<parameter key="no_units" value="4"/>
<parameter key="activation_function" value="'sigmoid'"/>
<parameter key="target_shape" value="1.1"/>
<parameter key="dims" value="1.1"/>
<parameter key="repetition_factor" value="2"/>
</operator>
<connect from_op="Add Convolutional Layer (4)" from_port="layers 1" to_op="Add Pooling Layer (4)" to_port="layers"/>
<connect from_op="Add Pooling Layer (4)" from_port="layers 1" to_op="Add Convolutional Layer (3)" to_port="layers"/>
<connect from_op="Add Convolutional Layer (3)" from_port="layers 1" to_op="Add Pooling Layer (3)" to_port="layers"/>
<connect from_op="Add Pooling Layer (3)" from_port="layers 1" to_op="Add Convolutional Layer (5)" to_port="layers"/>
<connect from_op="Add Convolutional Layer (5)" from_port="layers 1" to_op="Add Pooling Layer (5)" to_port="layers"/>
<connect from_op="Add Convolutional Layer (6)" from_port="layers 1" to_op="Add Pooling Layer (6)" to_port="layers"/>
<connect from_op="Add Convolutional Layer (2)" from_port="layers 1" to_op="Add Pooling Layer (2)" to_port="layers"/>
<connect from_op="Add Pooling Layer (2)" from_port="layers 1" to_op="Add Core Layer" to_port="layers"/>
<connect from_op="Add Core Layer" from_port="layers 1" to_op="Add Core Layer (3)" to_port="layers"/>
<connect from_op="Add Core Layer (3)" from_port="layers 1" to_op="Add Core Layer (5)" to_port="layers"/>
<connect from_op="Add Core Layer (5)" from_port="layers 1" to_op="Add Core Layer (11)" to_port="layers"/>
<connect from_op="Add Core Layer (11)" from_port="layers 1" to_op="Add Core Layer (7)" to_port="layers"/>
<connect from_op="Add Core Layer (7)" from_port="layers 1" to_port="layers 1"/>
<portSpacing port="sink_layers 1" spacing="0"/>
<portSpacing port="sink_layers 2" spacing="0"/>
<description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="1114" y="134">Il layer FLATTEN &#232; usato per Input a pi&#249; dimensioni e trasforma un layer a pi&#249; dimensioni in una</description>
</process>
</operator>
<operator activated="true" class="subprocess" compatibility="9.0.003" expanded="true" height="82" name="Normalize TestData" width="90" x="313" y="187">
<process expanded="true">
<operator activated="true" class="multiply" compatibility="9.0.003" expanded="true" height="103" name="Multiply (3)" width="90" x="45" y="136"/>
<operator activated="true" class="select_attributes" compatibility="9.0.003" expanded="true" height="82" name="Select Attributes (6)" width="90" x="246" y="187">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="KP"/>
</operator>
<operator activated="true" class="generate_id" compatibility="9.0.003" expanded="true" height="82" name="Generate ID (6)" width="90" x="782" y="187"/>
<operator activated="true" class="select_attributes" compatibility="9.0.003" expanded="true" height="82" name="Select Attributes (7)" width="90" x="246" y="34">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="|Z|Y|X|W|V|U|T|S|R|Q|P|O|N|M|L|KO|KN|KM|KL|KK|KJ|KI|KH|KG|KF|KE|KD|KC|KB|KA|K|JZ|JY|JX|JW|JV|JU|JT|JS|JR|JQ|JP|JO|JN|JM|JL|JK|JJ|JI|JH|JG|JF|JE|JD|JC|JB|JA|J|IZ|IY|IX|IW|IV|IU|IT|IS|IR|IQ|IP|IO|IN|IM|IL|IK|IJ|II|IH|IG|IF|IE|ID|IC|IB|IA|I|HZ|HY|HX|HW|HV|HU|HT|HS|HR|HQ|HP|HO|HN|HM|HL|HK|HJ|HI|HH|HG|HF|HE|HD|HC|HB|HA|H|GZ|GY|GX|GW|GV|GU|GT|GS|GR|GQ|GP|GO|GN|GM|GL|GK|GJ|GI|GH|GG|GF|GE|GD|GC|GB|GA|G|FZ|FY|FX|FW|FV|FU|FT|FS|FR|FQ|FP|FO|FN|FM|FL|FK|FJ|FI|FH|FG|FF|FE|FD|FC|FB|FA|F|EZ|EY|EX|EW|EV|EU|ET|ES|ER|EQ|EP|EO|EN|EM|EL|EK|EJ|EI|EH|EG|EF|EE|ED|EC|EB|EA|E|DZ|DY|DX|DW|DV|DU|DT|DS|DR|DQ|DP|DO|DN|DM|DL|DK|DJ|DI|DH|DG|DF|DE|DD|DC|DB|DA|D|CZ|CY|CX|CW|CV|CU|CT|CS|CR|CQ|CP|CO|CN|CM|CL|CK|CJ|CI|CH|CG|CF|CE|CD|CC|CB|CA|C|BZ|BY|BX|BW|BV|BU|BT|BS|BR|BQ|BP|BO|BN|BM|BL|BK|BJ|BI|BH|BG|BF|BE|BD|BC|BB|BA|B|AZ|AY|AX|AW|AV|AU|AT|AS|AR|AQ|AP|AO|AN|AM|AL|AK|AJ|AI|AH|AG|AF|AE|AD|AC|AB|AA|A"/>
</operator>
<operator activated="true" class="normalize" compatibility="9.0.003" expanded="true" height="103" name="Range Trasformation (3)" origin="GENERATED_SAMPLE" width="90" x="380" y="34">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attribute" value="KP"/>
<parameter key="attributes" value="|Z|Y|X|W|V|U|T|S|R|Q|P|O|N|M|L|KO|KN|KM|KL|KK|KJ|KI|KH|KG|KF|KE|KD|KC|KB|KA|K|JZ|JY|JX|JW|JV|JU|JT|JS|JR|JQ|JP|JO|JN|JM|JL|JK|JJ|JI|JH|JG|JF|JE|JD|JC|JB|JA|J|IZ|IY|IX|IW|IV|IU|IT|IS|IR|IQ|IP|IO|IN|IM|IL|IK|IJ|II|IH|IG|IF|IE|ID|IC|IB|IA|I|HZ|HY|HX|HW|HV|HU|HT|HS|HR|HQ|HP|HO|HN|HM|HL|HK|HJ|HI|HH|HG|HF|HE|HD|HC|HB|HA|H|GZ|GY|GX|GW|GV|GU|GT|GS|GR|GQ|GP|GO|GN|GM|GL|GK|GJ|GI|GH|GG|GF|GE|GD|GC|GB|GA|G|FZ|FY|FX|FW|FV|FU|FT|FS|FR|FQ|FP|FO|FN|FM|FL|FK|FJ|FI|FH|FG|FF|FE|FD|FC|FB|FA|F|EZ|EY|EX|EW|EV|EU|ET|ES|ER|EQ|EP|EO|EN|EM|EL|EK|EJ|EI|EH|EG|EF|EE|ED|EC|EB|EA|E|DZ|DY|DX|DW|DV|DU|DT|DS|DR|DQ|DP|DO|DN|DM|DL|DK|DJ|DI|DH|DG|DF|DE|DD|DC|DB|DA|D|CZ|CY|CX|CW|CV|CU|CT|CS|CR|CQ|CP|CO|CN|CM|CL|CK|CJ|CI|CH|CG|CF|CE|CD|CC|CB|CA|C|BZ|BY|BX|BW|BV|BU|BT|BS|BR|BQ|BP|BO|BN|BM|BL|BK|BJ|BI|BH|BG|BF|BE|BD|BC|BB|BA|B|AZ|AY|AX|AW|AV|AU|AT|AS|AR|AQ|AP|AO|AN|AM|AL|AK|AJ|AI|AH|AG|AF|AE|AD|AC|AB|AA|A"/>
<parameter key="method" value="range transformation"/>
</operator>
<operator activated="true" class="select_attributes" compatibility="9.0.003" expanded="true" height="82" name="Select Attributes (8)" width="90" x="514" y="34">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="KP"/>
<parameter key="invert_selection" value="true"/>
</operator>
<operator activated="true" class="series:windowing" compatibility="7.4.000" expanded="true" height="82" name="Windowing (4)" origin="GENERATED_SAMPLE" width="90" x="648" y="34">
<parameter key="window_size" value="1"/>
<parameter key="label_attribute" value="Close"/>
<parameter key="stop_on_too_small_dataset" value="false"/>
</operator>
<operator activated="true" class="generate_id" compatibility="9.0.003" expanded="true" height="82" name="Generate ID (7)" width="90" x="782" y="34"/>
<operator activated="true" class="concurrency:join" compatibility="9.0.003" expanded="true" height="82" name="Join PCA_Corr (3)" width="90" x="983" y="85">
<parameter key="use_id_attribute_as_key" value="true"/>
<list key="key_attributes"/>
</operator>
<connect from_port="in 1" to_op="Multiply (3)" to_port="input"/>
<connect from_op="Multiply (3)" from_port="output 1" to_op="Select Attributes (7)" to_port="example set input"/>
<connect from_op="Multiply (3)" from_port="output 2" to_op="Select Attributes (6)" to_port="example set input"/>
<connect from_op="Select Attributes (6)" from_port="example set output" to_op="Generate ID (6)" to_port="example set input"/>
<connect from_op="Generate ID (6)" from_port="example set output" to_op="Join PCA_Corr (3)" to_port="right"/>
<connect from_op="Select Attributes (7)" from_port="example set output" to_op="Range Trasformation (3)" to_port="example set input"/>
<connect from_op="Range Trasformation (3)" from_port="example set output" to_op="Select Attributes (8)" to_port="example set input"/>
<connect from_op="Select Attributes (8)" from_port="example set output" to_op="Windowing (4)" to_port="example set input"/>
<connect from_op="Windowing (4)" from_port="example set output" to_op="Generate ID (7)" to_port="example set input"/>
<connect from_op="Generate ID (7)" from_port="example set output" to_op="Join PCA_Corr (3)" to_port="left"/>
<connect from_op="Join PCA_Corr (3)" from_port="join" to_port="out 1"/>
<portSpacing port="source_in 1" spacing="0"/>
<portSpacing port="source_in 2" spacing="0"/>
<portSpacing port="sink_out 1" spacing="0"/>
<portSpacing port="sink_out 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="set_role" compatibility="9.0.003" expanded="true" height="82" name="Set Role Test" origin="GENERATED_SAMPLE" width="90" x="447" y="187">
<parameter key="attribute_name" value="KP"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="keras:apply" compatibility="1.0.003" expanded="true" height="82" name="Apply Keras Model" origin="GENERATED_SAMPLE" width="90" x="849" y="187"/>
<operator activated="true" class="performance" compatibility="9.0.003" expanded="true" height="82" name="Performance" width="90" x="983" y="187">
<parameter key="use_example_weights" value="false"/>
</operator>
<connect from_op="LoadData" from_port="out 1" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Windowing (3)" to_port="example set input"/>
<connect from_op="Windowing (3)" from_port="example set output" to_op="Cross Validation Keras" to_port="example set"/>
<connect from_op="Load TestData 300examples" from_port="out 1" to_op="Normalize TestData" to_port="in 1"/>
<connect from_op="Load TrainingData 900Examples" from_port="out 1" to_op="Normalize TrainData" to_port="in 1"/>
<connect from_op="Normalize TrainData" from_port="out 1" to_op="Set Role Train" to_port="example set input"/>
<connect from_op="Set Role Train" from_port="example set output" to_op="Keras Model" to_port="training set"/>
<connect from_op="Keras Model" from_port="model" to_op="Apply Keras Model" to_port="model"/>
<connect from_op="Normalize TestData" from_port="out 1" to_op="Set Role Test" to_port="example set input"/>
<connect from_op="Set Role Test" from_port="example set output" to_op="Apply Keras Model" to_port="unlabelled data"/>
<connect from_op="Apply Keras Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>
Hi, for the university stage I have to classify 4 groups of different signals, and associate them with 4 labels.
Using the cross-validation for the regression classifier and the ANN neural networks, the results are very good (average recognition of 90%).
My goal, however, is to use CNN networks through keras model. In this situation the recognition is not stable, it will change for the same data at each run and is not good like ANN.
For each material I have 1200 examples that are divided between training (900 examples) and testing (300 examples).
If I send you my process.xml, could you try to undestand why the CNN configuration it's not good enought?
Or can you suggest me the best CNN configuration i should use?
The input size is 301 attribute + special attribute(label).
In keras model I used:
- a convolution level with a 64 size filter;
- a level of pooling with layer type MaxPoolind1D;
- a dense level at 40;
- a dense level at 20;
- a dense level at 4;
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0
Answers
This is an issue that comes up often with Tensorflow. I suggest that you take a look at this video which discusses just this issue. You will see that it is not so straight forward. The video is very good and is necessary to understand when trying to get reproducable results in general. Your results will be very data dependent.
https://www.youtube.com/watch?v=Ys8ofBeR2kA
Kind regards,
Alex