dense_net = Network(
name='1_dense',
dimensions=(None, int(train_images.shape[1])),
input_var=input_var,
y=y,
config=network_dense_config,
input_network={'network': conv_net, 'layer': 4, 'get_params': True},
num_classes=10,
activation='rectify',
pred_activation='softmax'
)
train_images = np.expand_dims(train_images, axis=1)
test_images = np.expand_dims(test_images, axis=1)
# # Use to load model from disk
# # dense_net = Network.load_model('models/20170704194033_3_dense_test.network')
dense_net.train(
epochs=200,
train_x=train_images[:50000],
train_y=train_labels[:50000],
val_x=train_images[50000:60000],
val_y=train_labels[50000:60000],
batch_ratio=0.05,
plot=False
)
dense_net.save_record()
run_test(dense_net, test_x=test_images, test_y=test_labels)
dense_net.save_model()
'dropouts': [0.2],
}
dense_net = Network(name='3_dense_test',
dimensions=[None] + list(train_images.shape[1:]),
input_var=input_var,
y=y,
config=network_dense_config,
input_network=None,
num_classes=10,
activation='rectify',
pred_activation='softmax',
optimizer='adam')
# # Use to load model from disk
# # dense_net = Network.load_model('models/20170704194033_3_dense_test.network')
dense_net.train(epochs=2,
train_x=train_images[:50000],
train_y=train_labels[:50000],
val_x=train_images[50000:60000],
val_y=train_labels[50000:60000],
batch_ratio=0.05,
plot=True)
dense_net.save_record()
run_test(dense_net,
test_x=train_images[50000:60000],
test_y=train_labels[50000:60000])
dense_net.save_model()
y=y,
config=network_dense_config,
input_network={
'network': conv_net,
'layer': 4,
'get_params': True
},
num_classes=10,
activation='rectify',
pred_activation='softmax')
ensemble_dense = Snapshot(name='snap_test',
template_network=dense_net,
n_snapshots=5)
train_images = np.expand_dims(train_images, axis=1)
test_images = np.expand_dims(test_images, axis=1)
ensemble_dense.train(epochs=500,
train_x=train_images[:50000],
train_y=train_labels[:50000],
val_x=train_images[50000:60000],
val_y=train_labels[50000:60000],
batch_ratio=0.05,
plot=False)
ensemble_dense.save_record()
# ensemble_dense = Snapshot.load_ensemble('models/20170713183810_snap1')
run_test(ensemble_dense, test_x=test_images, test_y=test_labels)
ensemble_dense.save_model()