<jupyter_start><jupyter_text>CNN<jupyter_code>cnn_model = util_misc.load_ordered_yaml('../misc_files/cnn_ptrs_1.yaml')
cnn = lib_cnnPTRS.cnnPTRS(cnn_model, data_scheme, 'temp.h5', normalizer = True)
cnn.model.summary()
cnn_model = util_misc.load_ordered_yaml('../misc_files/cnn_ptrs_2.yaml')
cnn = lib_cnnPTRS.cnnPTRS(cnn_model, data_scheme, 'temp.h5', normalizer = True)
cnn.model.summary()<jupyter_output>Model: "model_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_3 (InputLayer)            [(None, 7044, 1)]    0                                            
__________________________________________________________________________________________________
layer1_conv (Conv1D)            (None, 3519, 32)     288         input_3[0][0]                    
__________________________________________________________________________________________________
layer1_maxpool (MaxPooling1D)   (None, 1759, 32)     0           layer1_conv[0][0]                
__________________________________________________________________________________________________
layer2_conv (Conv1D)            (None, 876, 32)      8224        layer1_maxpool[0][0]       [...]<jupyter_text>MLP<jupyter_code>mlp_model = util_misc.load_ordered_yaml('../misc_files/mlp_ptrs_1.yaml')
mlp = lib_cnnPTRS.mlpPTRS(mlp_model, data_scheme, 'temp.h5', normalizer = True)
mlp.model.summary()
mlp_model = util_misc.load_ordered_yaml('../misc_files/mlp_ptrs_2.yaml')
mlp = lib_cnnPTRS.mlpPTRS(mlp_model, data_scheme, 'temp.h5', normalizer = True)
mlp.model.summary()<jupyter_output>Model: "model_3"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_7 (InputLayer)            [(None, 7044, 1)]    0                                            
__________________________________________________________________________________________________
flatten_3 (Flatten)             (None, 7044)         0           input_7[0][0]                    
__________________________________________________________________________________________________
layer1_dense (Dense)            (None, 100)          704500      flatten_3[0][0]                  
__________________________________________________________________________________________________
layer2_dense (Dense)            (None, 50)           5050        layer1_dense[0][0]         [...]<jupyter_text>Baseline<jupyter_code>mlp = lib_cnnPTRS.mlpPTRS(None, data_scheme, 'temp.h5', normalizer = True)
mlp.model.summary()<jupyter_output>Model: "model_4"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_9 (InputLayer)            [(None, 7044, 1)]    0                                            
__________________________________________________________________________________________________
flatten_4 (Flatten)             (None, 7044)         0           input_9[0][0]                    
__________________________________________________________________________________________________
input_10 (InputLayer)           [(None, 25)]         0                                            
__________________________________________________________________________________________________
ptrs_dense_0 (Dense)            (None, 1)            7044        flatten_4[0][0]            [...]
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