Issue | #Downvotes for this reason | By |
---|
keras.wrappers.scikit_learn.KerasClassifier(2) | Automatically created scikit-learn flow. |
keras.wrappers.scikit_learn.KerasClassifier(2)_batch_size | 128 |
keras.wrappers.scikit_learn.KerasClassifier(2)_build_fn | "def squeezenet_emnist():\n input_layer = Input(shape=(784,), name=\"input\")\n reshape_layer=Reshape((28, 28,1))(input_layer)\n conv1 = Convolution2D(96, 3, 3, activation='relu', init='glorot_uniform',subsample=(2,2),border_mode='valid')(reshape_layer)\n maxpool1 = MaxPooling2D(pool_size=(2,2))(conv1)\n fire2_squeeze = Convolution2D(16, 1, 1, activation='relu', init='glorot_uniform',border_mode='same')(maxpool1)\n fire2_expand1 = Convolution2D(64, 1, 1, activation='relu', init='glorot_uniform',border_mode='same')(fire2_squeeze)\n fire2_expand2 = Convolution2D(64, 3, 3, activation='relu', init='glorot_uniform',border_mode='same')(fire2_squeeze)\n merge1 = merge(inputs=[fire2_expand1, fire2_expand2], mode=\"concat\", concat_axis=1)\n fire2 = Activation(\"linear\")(merge1)\n fire3_squeeze = Convolution2D(16, 1, 1, activation='relu', init='glorot_uniform',border_mode='same')(fire2)\n fire3_expand1 = Convolution2D(64, 1, 1, activation='relu', init='glorot_uniform',border_mode='same')(fire3_squeeze)\n fire3_expand2 = Convolution2D(64, 3, 3, activation='relu', init='glorot_uniform',border_mode='same')(fire3_squeeze)\n merge2 = merge(inputs=[fire3_expand1, fire3_expand2], mode=\"concat\", concat_axis=1)\n fire3 = Activation(\"linear\")(merge2)\n fire4_squeeze = Convolution2D(32, 1, 1, activation='relu', init='glorot_uniform',border_mode='same')(fire3)\n fire4_expand1 = Convolution2D(128, 1, 1, activation='relu', init='glorot_uniform',border_mode='same')(fire4_squeeze)\n fire4_expand2 = Convolution2D(128, 3, 3, activation='relu', init='glorot_uniform',border_mode='same')(fire4_squeeze)\n merge3 = merge(inputs=[fire4_expand1, fire4_expand2], mode=\"concat\", concat_axis=1)\n fire4 = Activation(\"linear\")(merge3)\n maxpool4 = MaxPooling2D((2,2))(fire4)\n fire5_squeeze = Convolution2D(32, 1, 1, activation='relu', init='glorot_uniform',border_mode='same')(maxpool4)\n fire5_expand1 = Convolution2D(128, 1, 1, activation='relu', init='glorot_uniform'," |
keras.wrappers.scikit_learn.KerasClassifier(2)_epochs | 5 |
keras.wrappers.scikit_learn.KerasClassifier(2)_verbose | 1 |
0.996 ± 0.0004 Per class Cross-validation details (10-fold Crossvalidation)
|
0.8475 ± 0.0146 Per class Cross-validation details (10-fold Crossvalidation)
|
0.8449 ± 0.0137 Cross-validation details (10-fold Crossvalidation)
|
118112.9063 ± 113.7821 Cross-validation details (10-fold Crossvalidation)
|
0.0083 ± 0.0007 Cross-validation details (10-fold Crossvalidation)
|
0.0416 Cross-validation details (10-fold Crossvalidation)
|
131600 Per class Cross-validation details (10-fold Crossvalidation) |
0.8483 ± 0.0102 Per class Cross-validation details (10-fold Crossvalidation)
|
0.8482 ± 0.0134 Cross-validation details (10-fold Crossvalidation)
|
5.5546 Cross-validation details (10-fold Crossvalidation)
|
0.8482 ± 0.0134 Per class Cross-validation details (10-fold Crossvalidation)
|
0.2003 ± 0.0173 Cross-validation details (10-fold Crossvalidation)
|
0.1443 Cross-validation details (10-fold Crossvalidation)
|
0.0672 ± 0.0026 Cross-validation details (10-fold Crossvalidation)
|
0.4656 ± 0.0179 Cross-validation details (10-fold Crossvalidation)
|