Fixed docstring of create_to_hidden_layer_models in example emnist model.
@@ -156,30 +156,22 @@ def create_original_fedavg_cnn_model(only_digits=True):
def create_two_hidden_layer_model(only_digits=True, hidden_units=200):
"""Create a two hidden-layer fully connected neural network.
- When `only_digits=False`, the summary of returned model is
+ When `only_digits=True`, the summary of returned model is
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
- conv2d (Conv2D) (None, 26, 26, 32) 320
- _________________________________________________________________
- conv2d_1 (Conv2D) (None, 24, 24, 64) 18496
- _________________________________________________________________
- max_pooling2d (MaxPooling2D) (None, 12, 12, 64) 0
+ reshape (Reshape) (None, 784) 0
_________________________________________________________________
- dropout (Dropout) (None, 12, 12, 64) 0
- _________________________________________________________________
- flatten (Flatten) (None, 9216) 0
+ dense (Dense) (None, 200) 157000
_________________________________________________________________
- dense (Dense) (None, 128) 1179776
- _________________________________________________________________
- dropout_1 (Dropout) (None, 128) 0
+ dense_1 (Dense) (None, 200) 40200
_________________________________________________________________
- dense_1 (Dense) (None, 62) 7998
+ dense_2 (Dense) (None, 10) 2010
=================================================================
- Total params: 1,206,590
- Trainable params: 1,206,590
+ Total params: 199,210
+ Trainable params: 199,210
Non-trainable params: 0
Args:
