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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=protected-access
# pylint: disable=g-import-not-at-top
"""Utilities related to model visualization."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from tensorflow.python.util.tf_export import tf_export
try:
# pydot-ng is a fork of pydot that is better maintained.
import pydot_ng as pydot
except ImportError:
# pydotplus is an improved version of pydot
try:
import pydotplus as pydot
except ImportError:
# Fall back on pydot if necessary.
try:
import pydot
except ImportError:
pydot = None
def _check_pydot():
try:
# Attempt to create an image of a blank graph
# to check the pydot/graphviz installation.
pydot.Dot.create(pydot.Dot())
except Exception:
# pydot raises a generic Exception here,
# so no specific class can be caught.
raise ImportError('Failed to import pydot. You must install pydot'
' and graphviz for `pydotprint` to work.')
def model_to_dot(model, show_shapes=False, show_layer_names=True, rankdir='TB'):
"""Convert a Keras model to dot format.
Arguments:
model: A Keras model instance.
show_shapes: whether to display shape information.
show_layer_names: whether to display layer names.
rankdir: `rankdir` argument passed to PyDot,
a string specifying the format of the plot:
'TB' creates a vertical plot;
'LR' creates a horizontal plot.
Returns:
A `pydot.Dot` instance representing the Keras model.
"""
from tensorflow.python.keras._impl.keras.layers.wrappers import Wrapper
from tensorflow.python.keras._impl.keras.models import Sequential
_check_pydot()
dot = pydot.Dot()
dot.set('rankdir', rankdir)
dot.set('concentrate', True)
dot.set_node_defaults(shape='record')
if isinstance(model, Sequential):
if not model.built:
model.build()
model = model.model
layers = model.layers
# Create graph nodes.
for layer in layers:
layer_id = str(id(layer))
# Append a wrapped layer's label to node's label, if it exists.
layer_name = layer.name
class_name = layer.__class__.__name__
if isinstance(layer, Wrapper):
layer_name = '{}({})'.format(layer_name, layer.layer.name)
child_class_name = layer.layer.__class__.__name__
class_name = '{}({})'.format(class_name, child_class_name)
# Create node's label.
if show_layer_names:
label = '{}: {}'.format(layer_name, class_name)
else:
label = class_name
# Rebuild the label as a table including input/output shapes.
if show_shapes:
try:
outputlabels = str(layer.output_shape)
except AttributeError:
outputlabels = 'multiple'
if hasattr(layer, 'input_shape'):
inputlabels = str(layer.input_shape)
elif hasattr(layer, 'input_shapes'):
inputlabels = ', '.join([str(ishape) for ishape in layer.input_shapes])
else:
inputlabels = 'multiple'
label = '%s\n|{input:|output:}|{{%s}|{%s}}' % (label, inputlabels,
outputlabels)
node = pydot.Node(layer_id, label=label)
dot.add_node(node)
# Connect nodes with edges.
for layer in layers:
layer_id = str(id(layer))
for i, node in enumerate(layer._inbound_nodes):
node_key = layer.name + '_ib-' + str(i)
if node_key in model._network_nodes: # pylint: disable=protected-access
for inbound_layer in node.inbound_layers:
inbound_layer_id = str(id(inbound_layer))
layer_id = str(id(layer))
dot.add_edge(pydot.Edge(inbound_layer_id, layer_id))
return dot
@tf_export('keras.utils.plot_model')
def plot_model(model,
to_file='model.png',
show_shapes=False,
show_layer_names=True,
rankdir='TB'):
"""Converts a Keras model to dot format and save to a file.
Arguments:
model: A Keras model instance
to_file: File name of the plot image.
show_shapes: whether to display shape information.
show_layer_names: whether to display layer names.
rankdir: `rankdir` argument passed to PyDot,
a string specifying the format of the plot:
'TB' creates a vertical plot;
'LR' creates a horizontal plot.
"""
dot = model_to_dot(model, show_shapes, show_layer_names, rankdir)
_, extension = os.path.splitext(to_file)
if not extension:
extension = 'png'
else:
extension = extension[1:]
dot.write(to_file, format=extension)
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