| 54 | |
| 55 | |
| 56 | def get_model(inputs_shape): |
| 57 | ni = Input(inputs_shape) |
| 58 | nn = Dropout(keep=0.8)(ni) |
| 59 | nn = Dense(n_units=800, act=tf.nn.relu, |
| 60 | in_channels=784)(nn) # in_channels is optional in this case as it can be inferred by the previous layer |
| 61 | nn = Dropout(keep=0.8)(nn) |
| 62 | nn = Dense(n_units=800, act=tf.nn.relu, |
| 63 | in_channels=800)(nn) # in_channels is optional in this case as it can be inferred by the previous layer |
| 64 | nn = Dropout(keep=0.8)(nn) |
| 65 | nn = Dense(n_units=10, act=tf.nn.relu, |
| 66 | in_channels=800)(nn) # in_channels is optional in this case as it can be inferred by the previous layer |
| 67 | M = Model(inputs=ni, outputs=nn) |
| 68 | return M |
| 69 | |
| 70 | |
| 71 | class Model_Save_with_weights(CustomTestCase): |