def architecture(self):
""" Shows network's architecture in the terminal if
``verbose`` parameter is equal to ``True``.
"""
self.logs.title("Network's architecture")
values = []
for index, layer in enumerate(self.layers, start=1):
input_shape = preformat_layer_shape(layer.input_shape)
output_shape = preformat_layer_shape(layer.output_shape)
classname = layer.__class__.__name__
values.append((index, input_shape, classname, output_shape))
table.TableBuilder.show_full_table(
columns=[
table.Column(name="#"),
table.Column(name="Input shape"),
table.Column(name="Layer Type"),
table.Column(name="Output shape"),
],
values=values,
stdout=self.logs.write,
)
self.logs.newline()
def architecture(self):
"""
Shows network's architecture in the terminal if
``verbose`` parameter is equal to ``True``.
"""
if not is_sequential(self.connection):
raise TypeError("You can check architecture only for sequential "
"connections. For other types of connections it's "
"better to use the `neupy.plots.layer_structure` "
"function.")
self.logs.title("Network's architecture")
values = []
for index, layer in enumerate(self.layers, start=1):
input_shape = preformat_layer_shape(layer.input_shape)
output_shape = preformat_layer_shape(layer.output_shape)
classname = layer.__class__.__name__
values.append((index, input_shape, classname, output_shape))
table.TableBuilder.show_full_table(
columns=[
table.Column(name="#"),
table.Column(name="Input shape"),
table.Column(name="Layer Type"),
table.Column(name="Output shape"),
],
values=values,
stdout=self.logs.write,
)
self.logs.newline()
def architecture(self):
"""
Shows network's architecture in the terminal if
``verbose`` parameter is equal to ``True``.
"""
if not is_sequential(self.connection):
raise TypeError("You can check architecture only for sequential "
"connections. For other types of connections it's "
"better to use the `neupy.plots.layer_structure` "
"function.")
self.logs.title("Network's architecture")
values = []
for index, layer in enumerate(self.layers, start=1):
input_shape = preformat_layer_shape(layer.input_shape)
output_shape = preformat_layer_shape(layer.output_shape)
classname = layer.__class__.__name__
values.append((index, input_shape, classname, output_shape))
table.TableBuilder.show_full_table(
columns=[
table.Column(name="#"),
table.Column(name="Input shape"),
table.Column(name="Layer Type"),
table.Column(name="Output shape"),
],
values=values,
stdout=self.logs.write,
)
self.logs.newline()
def __repr__(self):
n_layers = len(self)
if n_layers > 5 or not is_sequential(self):
return '{} -> [... {} layers ...] -> {}'.format(
preformat_layer_shape(self.input_shape), n_layers,
preformat_layer_shape(self.output_shape))
return ' > '.join([repr(layer) for layer in self])
def __repr__(self):
n_layers = len(self)
if n_layers > 5 or not is_sequential(self):
conn = '{} -> [... {} layers ...] -> {}'.format(
preformat_layer_shape(self.input_shape),
n_layers,
preformat_layer_shape(self.output_shape)
)
else:
conn = ' > '.join([repr(layer) for layer in self])
return conn
def architecture(self):
"""
Shows network's architecture in the terminal if
``verbose`` parameter is equal to ``True``.
"""
if not is_sequential(self.connection):
raise TypeError("You can check architecture only for sequential "
"connections. For other types of connections "
"it's better to use the "
"`neupy.plots.network_structure` function.")
self.logs.title("Network's architecture")
values = []
for index, layer in enumerate(self.layers, start=1):
input_shape = preformat_layer_shape(layer.input_shape)
output_shape = preformat_layer_shape(layer.output_shape)
classname = layer.__class__.__name__
values.append((index, input_shape, classname, output_shape))
self.logs.table(
values, headers=['#', 'Input shape', 'Layer type', 'Output shape'])
self.logs.newline()
def test_preformat_layer_shape(self):
self.assertEqual((2, 3, 1), preformat_layer_shape((2, 3, 1)))
self.assertEqual(10, preformat_layer_shape((10, )))
def test_preformat_layer_shape(self):
self.assertEqual((2, 3, 1), preformat_layer_shape((2, 3, 1)))
self.assertEqual(10, preformat_layer_shape((10,)))
