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Commit b15c7d46 authored by Yusei Tahara's avatar Yusei Tahara
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erp5_wendelin_examples_keras: Add primitive examples of Keras in wendelin.

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bt5/erp5_wendelin_examples_keras/ExtensionTemplateItem/portal_components/keras_save_load.py 0 → 100644
View file @ b15c7d46
import warnings
import numpy as np
from keras import backend as K
from keras import __version__ as keras_version
from keras.models import Sequential
from keras.models import model_from_config
from keras.optimizers import optimizer_from_config
from keras import optimizers
def save_model(model, model_store=None):
data = {}
data['keras_version'] = keras_version
data['model_config'] = {'class_name':model.__class__.__name__,
'config':model.get_config()}
# save weights
if hasattr(model, 'flattened_layers'):
# Support for legacy Sequential/Merge behavior.
flattened_layers = model.flattened_layers
else:
flattened_layers = model.layers
data['layer_names'] = [layer.name for layer in flattened_layers]
layer_group = {}
for layer in flattened_layers:
group = layer_group[layer.name] = {}
symbolic_weights = layer.weights
weight_values = K.batch_get_value(symbolic_weights)
weight_names = []
for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)):
if hasattr(w, 'name') and w.name:
name = str(w.name)
else:
name = 'param_' + str(i)
weight_names.append(name)
group['weight_names'] = weight_names
group['weight_values'] = []
for name, val in zip(weight_names, weight_values):
group['weight_values'].append(val.copy())
data['model_weights'] = layer_group
if hasattr(model, 'optimizer'):
if isinstance(model.optimizer, optimizers.TFOptimizer):
warnings.warn(
'TensorFlow optimizers do not '
'make it possible to access '
'optimizer attributes or optimizer state '
'after instantiation. '
'As a result, we cannot save the optimizer '
'as part of the model save file.'
'You will have to compile your model again after loading it. '
'Prefer using a Keras optimizer instead '
'(see keras.io/optimizers).')
else:
data['training_config'] = {
'optimizer_config':{
'class_name':model.optimizer.__class__.__name__,
'config':model.optimizer.get_config()},
'loss': model.loss,
'metrics': model.metrics,
'sample_weight_mode': model.sample_weight_mode,
'loss_weights': model.loss_weights,
}
# save optimizer weights
symbolic_weights = getattr(model.optimizer, 'weights')
if symbolic_weights:
data['optimizer_weights'] = {}
weight_values = K.batch_get_value(symbolic_weights)
weight_names = []
for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)):
if hasattr(w, 'name') and w.name:
name = str(w.name)
else:
name = 'param_' + str(i)
weight_names.append(name)
data['optimizer_weights']['weight_names'] = weight_names
data['optimizer_weights']['weight_values'] = []
for name, val in zip(weight_names, weight_values):
data['optimizer_weights']['weight_values'].append(val.copy())
return data
def load_model(data):
# instantiate model
model_config = data['model_config']
if model_config is None:
raise ValueError('No model found in config file.')
model = model_from_config(model_config)
if hasattr(model, 'flattened_layers'):
# Support for legacy Sequential/Merge behavior.
flattened_layers = model.flattened_layers
else:
flattened_layers = model.layers
filtered_layers = []
for layer in flattened_layers:
weights = layer.weights
if weights:
filtered_layers.append(layer)
flattened_layers = filtered_layers
layer_names = data['layer_names']
filtered_layer_names = []
for name in layer_names:
weight_dict = data['model_weights'][name]
weight_names = weight_dict['weight_names']
if len(weight_names):
filtered_layer_names.append(name)
layer_names = filtered_layer_names
if len(layer_names) != len(flattened_layers):
raise ValueError('You are trying to load a weight file '
'containing ' + str(len(layer_names)) +
' layers into a model with ' +
str(len(flattened_layers)) + ' layers.')
# We batch weight value assignments in a single backend call
# which provides a speedup in TensorFlow.
weight_value_tuples = []
for k, name in enumerate(layer_names):
weight_dict = data['model_weights'][name]
weight_names = weight_dict['weight_names']
weight_values = weight_dict['weight_values']
layer = flattened_layers[k]
symbolic_weights = layer.weights
if len(weight_values) != len(symbolic_weights):
raise ValueError('Layer #' + str(k) +
' (named "' + layer.name +
'" in the current model) was found to '
'correspond to layer ' + name +
' in the save file. '
'However the new layer ' + layer.name +
' expects ' + str(len(symbolic_weights)) +
' weights, but the saved weights have ' +
str(len(weight_values)) +
' elements.')
if layer.__class__.__name__ == 'Convolution1D':
# This is for backwards compatibility with
# the old Conv1D weights format.
w = weight_values[0]
shape = w.shape
if shape[:2] != (layer.filter_length, 1) or shape[3] != layer.nb_filter:
# Legacy shape:
# (self.nb_filter, input_dim, self.filter_length, 1)
assert shape[0] == layer.nb_filter and shape[2:] == (layer.filter_length, 1)
w = np.transpose(w, (2, 3, 1, 0))
weight_values[0] = w
weight_value_tuples += zip(symbolic_weights, weight_values)
K.batch_set_value(weight_value_tuples)
# instantiate optimizer
training_config = data.get('training_config')
if training_config is None:
warnings.warn('No training configuration found in save file: '
'the model was *not* compiled. Compile it manually.')
return model
optimizer_config = training_config['optimizer_config']
optimizer = optimizer_from_config(optimizer_config)
# recover loss functions and metrics
loss = training_config['loss']
metrics = training_config['metrics']
sample_weight_mode = training_config['sample_weight_mode']
loss_weights = training_config['loss_weights']
# compile model
model.compile(optimizer=optimizer,
loss=loss,
metrics=metrics,
loss_weights=loss_weights,
sample_weight_mode=sample_weight_mode)
# set optimizer weights
if 'optimizer_weights' in data:
# build train function (to get weight updates)
if isinstance(model, Sequential):
model.model._make_train_function()
else:
model._make_train_function()
optimizer_weights_dict = data['optimizer_weights']
optimizer_weight_names = optimizer_weights_dict['weight_names']
optimizer_weight_values = optimizer_weights_dict['weight_values']
model.optimizer.set_weights(optimizer_weight_values)
return model
\ No newline at end of file
bt5/erp5_wendelin_examples_keras/ExtensionTemplateItem/portal_components/keras_save_load.xml 0 → 100644
View file @ b15c7d46
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="Extension Component" module="erp5.portal_type"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>default_reference</string> </key>
<value> <string>keras_save_load</string> </value>
</item>
<item>
<key> <string>description</string> </key>
<value>
<none/>
</value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>keras_save_load</string> </value>
</item>
<item>
<key> <string>portal_type</string> </key>
<value> <string>Extension Component</string> </value>
</item>
<item>
<key> <string>sid</string> </key>
<value>
<none/>
</value>
</item>
<item>
<key> <string>text_content_error_message</string> </key>
<value>
<tuple/>
</value>
</item>
<item>
<key> <string>text_content_warning_message</string> </key>
<value>
<tuple>
<string>W:182, 4: Unused variable \'optimizer_weight_names\' (unused-variable)</string>
</tuple>
</value>
</item>
<item>
<key> <string>version</string> </key>
<value> <string>erp5</string> </value>
</item>
<item>
<key> <string>workflow_history</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAI=</string> </persistent>
</value>
</item>
</dictionary>
</pickle>
</record>
<record id="2" aka="AAAAAAAAAAI=">
<pickle>
<global name="PersistentMapping" module="Persistence.mapping"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>data</string> </key>
<value>
<dictionary>
<item>
<key> <string>component_validation_workflow</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAM=</string> </persistent>
</value>
</item>
</dictionary>
</value>
</item>
</dictionary>
</pickle>
</record>
<record id="3" aka="AAAAAAAAAAM=">
<pickle>
<global name="WorkflowHistoryList" module="Products.ERP5Type.patches.WorkflowTool"/>
</pickle>
<pickle>
<tuple>
<none/>
<list>
<dictionary>
<item>
<key> <string>action</string> </key>
<value> <string>validate</string> </value>
</item>
<item>
<key> <string>validation_state</string> </key>
<value> <string>validated</string> </value>
</item>
</dictionary>
</list>
</tuple>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/ExtensionTemplateItem/portal_components/keras_train_model.py 0 → 100644
View file @ b15c7d46
import numpy as np
import time
import sys
import transaction
class Progbar(object):
def output(self, data):
self.output1(str(data))
def __init__(self, target, width=30, verbose=1, interval=0.01, output=None):
"""Dislays a progress bar.
# Arguments:
target: Total number of steps expected.
interval: Minimum visual progress update interval (in seconds).
"""
self.width = width
self.target = target
self.sum_values = {}
self.unique_values = []
self.start = time.time()
self.last_update = 0
self.interval = interval
self.total_width = 0
self.seen_so_far = 0
self.verbose = verbose
self.output1 = output
def update(self, current, values=[], force=False):
"""Updates the progress bar.
# Arguments
current: Index of current step.
values: List of tuples (name, value_for_last_step).
The progress bar will display averages for these values.
force: Whether to force visual progress update.
"""
for k, v in values:
if k not in self.sum_values:
self.sum_values[k] = [v * (current - self.seen_so_far),
current - self.seen_so_far]
self.unique_values.append(k)
else:
self.sum_values[k][0] += v * (current - self.seen_so_far)
self.sum_values[k][1] += (current - self.seen_so_far)
self.seen_so_far = current
now = time.time()
if self.verbose == 1:
if not force and (now - self.last_update) < self.interval:
return
prev_total_width = self.total_width
#self.output('\b' * prev_total_width)
self.output('\r')
numdigits = int(np.floor(np.log10(self.target))) + 1
barstr = '%%%dd/%%%dd [' % (numdigits, numdigits)
bar = barstr % (current, self.target)
prog = float(current) / self.target
prog_width = int(self.width * prog)
if prog_width > 0:
bar += ('=' * (prog_width - 1))
if current < self.target:
bar += '>'
else:
bar += '='
bar += ('.' * (self.width - prog_width))
bar += ']'
self.output(bar)
self.total_width = len(bar)
if current:
time_per_unit = (now - self.start) / current
else:
time_per_unit = 0
eta = time_per_unit * (self.target - current)
info = ''
if current < self.target:
info += ' - ETA: %ds' % eta
else:
info += ' - %ds' % (now - self.start)
for k in self.unique_values:
info += ' - %s:' % k
if isinstance(self.sum_values[k], list):
avg = self.sum_values[k][0] / max(1, self.sum_values[k][1])
if abs(avg) > 1e-3:
info += ' %.4f' % avg
else:
info += ' %.4e' % avg
else:
info += ' %s' % self.sum_values[k]
self.total_width += len(info)
if prev_total_width > self.total_width:
info += ((prev_total_width - self.total_width) * ' ')
self.output(info)
if current >= self.target:
self.output('\r\n')
if self.verbose == 2:
if current >= self.target:
info = '%ds' % (now - self.start)
for k in self.unique_values:
info += ' - %s:' % k
avg = self.sum_values[k][0] / max(1, self.sum_values[k][1])
if avg > 1e-3:
info += ' %.4f' % avg
else:
info += ' %.4e' % avg
self.output(info + "\r\n")
self.last_update = now
def add(self, n, values=[]):
self.update(self.seen_so_far + n, values)
from keras.callbacks import ProgbarLogger as OriginalProgbarLogger
class ProgbarLogger(OriginalProgbarLogger):
def __init__(self, output, verbose=0):
self.output = output
self.verbose = verbose
def on_epoch_begin(self, epoch, logs=None):
if self.verbose:
self.output('Epoch %d/%d\r\n' % (epoch + 1, self.nb_epoch))
self.progbar = Progbar(target=self.params['nb_sample'],
verbose=1, output=self.output)
self.seen = 0
def on_epoch_end(self, epoch, logs=None):
super(ProgbarLogger, self).on_epoch_end(epoch, logs)
if epoch % 10 == 0:
transaction.commit()
seed = 7
np.random.seed(seed)
from cStringIO import StringIO
import cPickle
def save(portal, value):
data_stream = portal.data_stream_module.wendelin_examples_keras_nn
data_stream.edit(file=StringIO(cPickle.dumps(value)))
def load(portal):
data_stream = portal.data_stream_module.wendelin_examples_keras_nn
data = data_stream.getData()
if data:
return cPickle.loads(data)
else:
return None
def train(portal):
# This is just a demo of keras.
# 1. you can use keras.
# 2. you can save trained model.
# 3. you can load trained model.
from cStringIO import StringIO
import tensorflow as tf
sess = tf.Session()
from keras import backend as K
K.set_session(sess)
stream = portal.data_stream_module.wendelin_examples_keras_log
def output(value):
stream.appendData(value)
saved_model_data = load(portal)
if saved_model_data is not None:
model = portal.keras_load_model(saved_model_data)
else:
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
dataset = np.loadtxt(StringIO(str(portal.portal_skins.erp5_wendelin_examples_keras['pima.csv'])), delimiter=',')
X = dataset[:, 0:8]
Y = dataset[:, 8]
model.fit(X, Y, nb_epoch=20, batch_size=10, callbacks=[ProgbarLogger(output)])
scores = model.evaluate(X, Y)
output('%s: %.2f%%' % (model.metrics_names[1], scores[1]*100))
model_dict = portal.keras_save_model(model)
K.clear_session()
save(portal, model_dict)
return model_dict
bt5/erp5_wendelin_examples_keras/ExtensionTemplateItem/portal_components/keras_train_model.xml 0 → 100644
View file @ b15c7d46
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="Extension Component" module="erp5.portal_type"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>_recorded_property_dict</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAI=</string> </persistent>
</value>
</item>
<item>
<key> <string>default_reference</string> </key>
<value> <string>keras_train_model</string> </value>
</item>
<item>
<key> <string>description</string> </key>
<value>
<none/>
</value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>keras_train_model</string> </value>
</item>
<item>
<key> <string>portal_type</string> </key>
<value> <string>Extension Component</string> </value>
</item>
<item>
<key> <string>sid</string> </key>
<value>
<none/>
</value>
</item>
<item>
<key> <string>text_content_error_message</string> </key>
<value>
<tuple/>
</value>
</item>
<item>
<key> <string>text_content_warning_message</string> </key>
<value>
<tuple>
<string>W: 8, 0: Bad indentation. Found 4 spaces, expected 2 (bad-indentation)</string>
<string>W: 9, 0: Bad indentation. Found 6 spaces, expected 4 (bad-indentation)</string>
<string>W: 11, 0: Bad indentation. Found 4 spaces, expected 2 (bad-indentation)</string>
<string>W: 12, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 18, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 19, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 20, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 21, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 22, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 23, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 24, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 25, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 26, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 27, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 28, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 30, 0: Bad indentation. Found 4 spaces, expected 2 (bad-indentation)</string>
<string>W: 31, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 39, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 40, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 41, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 43, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 44, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 45, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 46, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 47, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 49, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 50, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 51, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 52, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 54, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 56, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 58, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 59, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 60, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 61, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 62, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 63, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 64, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 65, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 66, 0: Bad indentation. Found 20 spaces, expected 10 (bad-indentation)</string>
<string>W: 67, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 68, 0: Bad indentation. Found 20 spaces, expected 10 (bad-indentation)</string>
<string>W: 69, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 70, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 71, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 72, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 74, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 75, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 76, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 77, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 78, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 79, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 80, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 81, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 82, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 83, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 84, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 85, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 86, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 87, 0: Bad indentation. Found 20 spaces, expected 10 (bad-indentation)</string>
<string>W: 88, 0: Bad indentation. Found 20 spaces, expected 10 (bad-indentation)</string>
<string>W: 89, 0: Bad indentation. Found 24 spaces, expected 12 (bad-indentation)</string>
<string>W: 90, 0: Bad indentation. Found 20 spaces, expected 10 (bad-indentation)</string>
<string>W: 91, 0: Bad indentation. Found 24 spaces, expected 12 (bad-indentation)</string>
<string>W: 92, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 93, 0: Bad indentation. Found 20 spaces, expected 10 (bad-indentation)</string>
<string>W: 95, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 96, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W: 97, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W: 99, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W:101, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W:102, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W:104, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W:105, 0: Bad indentation. Found 12 spaces, expected 6 (bad-indentation)</string>
<string>W:106, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W:107, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W:108, 0: Bad indentation. Found 20 spaces, expected 10 (bad-indentation)</string>
<string>W:109, 0: Bad indentation. Found 20 spaces, expected 10 (bad-indentation)</string>
<string>W:110, 0: Bad indentation. Found 20 spaces, expected 10 (bad-indentation)</string>
<string>W:111, 0: Bad indentation. Found 24 spaces, expected 12 (bad-indentation)</string>
<string>W:112, 0: Bad indentation. Found 20 spaces, expected 10 (bad-indentation)</string>
<string>W:113, 0: Bad indentation. Found 24 spaces, expected 12 (bad-indentation)</string>
<string>W:114, 0: Bad indentation. Found 16 spaces, expected 8 (bad-indentation)</string>
<string>W:116, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W:118, 0: Bad indentation. Found 4 spaces, expected 2 (bad-indentation)</string>
<string>W:119, 0: Bad indentation. Found 8 spaces, expected 4 (bad-indentation)</string>
<string>W: 30, 4: Dangerous default value [] as argument (dangerous-default-value)</string>
<string>W:118, 4: Dangerous default value [] as argument (dangerous-default-value)</string>
<string>W:126, 2: __init__ method from base class \'ProgbarLogger\' is not called (super-init-not-called)</string>
<string>W:165, 2: Redefining name \'StringIO\' from outer scope (line 146) (redefined-outer-name)</string>
<string>W:165, 2: Reimport \'StringIO\' (imported line 146) (reimported)</string>
<string>W: 3, 0: Unused import sys (unused-import)</string>
</tuple>
</value>
</item>
<item>
<key> <string>version</string> </key>
<value> <string>erp5</string> </value>
</item>
<item>
<key> <string>workflow_history</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAM=</string> </persistent>
</value>
</item>
</dictionary>
</pickle>
</record>
<record id="2" aka="AAAAAAAAAAI=">
<pickle>
<global name="PersistentMapping" module="Persistence.mapping"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>data</string> </key>
<value>
<dictionary/>
</value>
</item>
</dictionary>
</pickle>
</record>
<record id="3" aka="AAAAAAAAAAM=">
<pickle>
<global name="PersistentMapping" module="Persistence.mapping"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>data</string> </key>
<value>
<dictionary>
<item>
<key> <string>component_validation_workflow</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAQ=</string> </persistent>
</value>
</item>
</dictionary>
</value>
</item>
</dictionary>
</pickle>
</record>
<record id="4" aka="AAAAAAAAAAQ=">
<pickle>
<global name="WorkflowHistoryList" module="Products.ERP5Type.patches.WorkflowTool"/>
</pickle>
<pickle>
<tuple>
<none/>
<list>
<dictionary>
<item>
<key> <string>action</string> </key>
<value> <string>validate</string> </value>
</item>
<item>
<key> <string>validation_state</string> </key>
<value> <string>validated</string> </value>
</item>
</dictionary>
</list>
</tuple>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/ExtensionTemplateItem/portal_components/keras_vgg16_predict.py 0 → 100644
View file @ b15c7d46
from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
from keras.preprocessing import image
import numpy as np
from cStringIO import StringIO
import PIL.Image
model = VGG16(weights='imagenet')
def predict(image_document):
img = PIL.Image.open(StringIO(image_document.getData()))
img = img.resize((224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
preds = model.predict(preprocess_input(x))
results = decode_predictions(preds, top=5)[0]
return results
bt5/erp5_wendelin_examples_keras/ExtensionTemplateItem/portal_components/keras_vgg16_predict.xml 0 → 100644
View file @ b15c7d46
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="Extension Component" module="erp5.portal_type"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>_recorded_property_dict</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAI=</string> </persistent>
</value>
</item>
<item>
<key> <string>default_reference</string> </key>
<value> <string>keras_vgg16_predict</string> </value>
</item>
<item>
<key> <string>description</string> </key>
<value>
<none/>
</value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>keras_vgg16_predict</string> </value>
</item>
<item>
<key> <string>portal_type</string> </key>
<value> <string>Extension Component</string> </value>
</item>
<item>
<key> <string>sid</string> </key>
<value>
<none/>
</value>
</item>
<item>
<key> <string>text_content_error_message</string> </key>
<value>
<tuple/>
</value>
</item>
<item>
<key> <string>text_content_warning_message</string> </key>
<value>
<tuple/>
</value>
</item>
<item>
<key> <string>version</string> </key>
<value> <string>erp5</string> </value>
</item>
<item>
<key> <string>workflow_history</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAM=</string> </persistent>
</value>
</item>
</dictionary>
</pickle>
</record>
<record id="2" aka="AAAAAAAAAAI=">
<pickle>
<global name="PersistentMapping" module="Persistence.mapping"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>data</string> </key>
<value>
<dictionary/>
</value>
</item>
</dictionary>
</pickle>
</record>
<record id="3" aka="AAAAAAAAAAM=">
<pickle>
<global name="PersistentMapping" module="Persistence.mapping"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>data</string> </key>
<value>
<dictionary>
<item>
<key> <string>component_validation_workflow</string> </key>
<value>
<persistent> <string encoding="base64">AAAAAAAAAAQ=</string> </persistent>
</value>
</item>
</dictionary>
</value>
</item>
</dictionary>
</pickle>
</record>
<record id="4" aka="AAAAAAAAAAQ=">
<pickle>
<global name="WorkflowHistoryList" module="Products.ERP5Type.patches.WorkflowTool"/>
</pickle>
<pickle>
<tuple>
<none/>
<list>
<dictionary>
<item>
<key> <string>action</string> </key>
<value> <string>validate</string> </value>
</item>
<item>
<key> <string>validation_state</string> </key>
<value> <string>validated</string> </value>
</item>
</dictionary>
</list>
</tuple>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras.xml 0 → 100644
View file @ b15c7d46
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="Folder" module="OFS.Folder"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>_objects</string> </key>
<value>
<tuple/>
</value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>erp5_wendelin_examples_keras</string> </value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/README.erp5_wendelin_examples_keras.txt.txt 0 → 100644
View file @ b15c7d46
You need a wendelin instance that contains keras.
Use this software release to built it.
https://lab.nexedi.com/nexedi/slapos/raw/master/software/wendelin/software-kerastensorflow.cfg
call_keras_vgg16_predict
--------------------------
You can use a trained neural network for image classification.
call_train_keras and call_read_keras_log
--------------------------------------------
You can train and save and load neural network. Call call_train_keras, you can run a model training and save the model in a data stream. Next time you run, it loads the model from the data stream and continue training. You can use call_read_keras_log to read keras output.
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/README.erp5_wendelin_examples_keras.txt.xml 0 → 100644
View file @ b15c7d46
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="File" module="OFS.Image"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>__name__</string> </key>
<value> <string>README.erp5_wendelin_examples_keras.txt</string> </value>
</item>
<item>
<key> <string>content_type</string> </key>
<value> <string>text/plain</string> </value>
</item>
<item>
<key> <string>precondition</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/call_keras_vgg16_predict.py 0 → 100644
View file @ b15c7d46
for i in context.keras_vgg16_predict(context.image_module[image_document_id]):
print i
return printed
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/call_keras_vgg16_predict.xml 0 → 100644
View file @ b15c7d46
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="PythonScript" module="Products.PythonScripts.PythonScript"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>Script_magic</string> </key>
<value> <int>3</int> </value>
</item>
<item>
<key> <string>_bind_names</string> </key>
<value>
<object>
<klass>
<global name="NameAssignments" module="Shared.DC.Scripts.Bindings"/>
</klass>
<tuple/>
<state>
<dictionary>
<item>
<key> <string>_asgns</string> </key>
<value>
<dictionary>
<item>
<key> <string>name_container</string> </key>
<value> <string>container</string> </value>
</item>
<item>
<key> <string>name_context</string> </key>
<value> <string>context</string> </value>
</item>
<item>
<key> <string>name_m_self</string> </key>
<value> <string>script</string> </value>
</item>
<item>
<key> <string>name_subpath</string> </key>
<value> <string>traverse_subpath</string> </value>
</item>
</dictionary>
</value>
</item>
</dictionary>
</state>
</object>
</value>
</item>
<item>
<key> <string>_params</string> </key>
<value> <string>image_document_id</string> </value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>call_keras_vgg16_predict</string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/call_read_keras_log.xml 0 → 100644
View file @ b15c7d46
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="PythonScript" module="Products.PythonScripts.PythonScript"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>Script_magic</string> </key>
<value> <int>3</int> </value>
</item>
<item>
<key> <string>_bind_names</string> </key>
<value>
<object>
<klass>
<global name="NameAssignments" module="Shared.DC.Scripts.Bindings"/>
</klass>
<tuple/>
<state>
<dictionary>
<item>
<key> <string>_asgns</string> </key>
<value>
<dictionary>
<item>
<key> <string>name_container</string> </key>
<value> <string>container</string> </value>
</item>
<item>
<key> <string>name_context</string> </key>
<value> <string>context</string> </value>
</item>
<item>
<key> <string>name_m_self</string> </key>
<value> <string>script</string> </value>
</item>
<item>
<key> <string>name_subpath</string> </key>
<value> <string>traverse_subpath</string> </value>
</item>
</dictionary>
</value>
</item>
</dictionary>
</state>
</object>
</value>
</item>
<item>
<key> <string>_params</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>call_read_keras_log</string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/call_train_keras.py 0 → 100644
View file @ b15c7d46
return context.organisation_module.keras_train_model(context.getPortalObject())
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/call_train_keras.xml 0 → 100644
View file @ b15c7d46
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="PythonScript" module="Products.PythonScripts.PythonScript"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>Script_magic</string> </key>
<value> <int>3</int> </value>
</item>
<item>
<key> <string>_bind_names</string> </key>
<value>
<object>
<klass>
<global name="NameAssignments" module="Shared.DC.Scripts.Bindings"/>
</klass>
<tuple/>
<state>
<dictionary>
<item>
<key> <string>_asgns</string> </key>
<value>
<dictionary>
<item>
<key> <string>name_container</string> </key>
<value> <string>container</string> </value>
</item>
<item>
<key> <string>name_context</string> </key>
<value> <string>context</string> </value>
</item>
<item>
<key> <string>name_m_self</string> </key>
<value> <string>script</string> </value>
</item>
<item>
<key> <string>name_subpath</string> </key>
<value> <string>traverse_subpath</string> </value>
</item>
</dictionary>
</value>
</item>
</dictionary>
</state>
</object>
</value>
</item>
<item>
<key> <string>_params</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>call_train_keras</string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/keras_load_model.xml 0 → 100644
View file @ b15c7d46
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="ExternalMethod" module="Products.ExternalMethod.ExternalMethod"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>_function</string> </key>
<value> <string>load_model</string> </value>
</item>
<item>
<key> <string>_module</string> </key>
<value> <string>keras_save_load</string> </value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>keras_load_model</string> </value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/keras_save_model.xml 0 → 100644
View file @ b15c7d46
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="ExternalMethod" module="Products.ExternalMethod.ExternalMethod"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>_function</string> </key>
<value> <string>save_model</string> </value>
</item>
<item>
<key> <string>_module</string> </key>
<value> <string>keras_save_load</string> </value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>keras_save_model</string> </value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/keras_train_model.xml 0 → 100644
View file @ b15c7d46
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="ExternalMethod" module="Products.ExternalMethod.ExternalMethod"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>_function</string> </key>
<value> <string>train</string> </value>
</item>
<item>
<key> <string>_module</string> </key>
<value> <string>keras_train_model</string> </value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>keras_train_model</string> </value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/keras_vgg16_predict.xml 0 → 100644
View file @ b15c7d46
<?xml version="1.0"?>
<ZopeData>
<record id="1" aka="AAAAAAAAAAE=">
<pickle>
<global name="ExternalMethod" module="Products.ExternalMethod.ExternalMethod"/>
</pickle>
<pickle>
<dictionary>
<item>
<key> <string>_function</string> </key>
<value> <string>predict</string> </value>
</item>
<item>
<key> <string>_module</string> </key>
<value> <string>keras_vgg16_predict</string> </value>
</item>
<item>
<key> <string>id</string> </key>
<value> <string>keras_vgg16_predict</string> </value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/pima.csv.csv 0 → 100644
View file @ b15c7d46
6,148,72,35,0,33.6,0.627,50,1
1,85,66,29,0,26.6,0.351,31,0
8,183,64,0,0,23.3,0.672,32,1
1,89,66,23,94,28.1,0.167,21,0
0,137,40,35,168,43.1,2.288,33,1
5,116,74,0,0,25.6,0.201,30,0
3,78,50,32,88,31.0,0.248,26,1
10,115,0,0,0,35.3,0.134,29,0
2,197,70,45,543,30.5,0.158,53,1
8,125,96,0,0,0.0,0.232,54,1
4,110,92,0,0,37.6,0.191,30,0
10,168,74,0,0,38.0,0.537,34,1
10,139,80,0,0,27.1,1.441,57,0
1,189,60,23,846,30.1,0.398,59,1
5,166,72,19,175,25.8,0.587,51,1
7,100,0,0,0,30.0,0.484,32,1
0,118,84,47,230,45.8,0.551,31,1
7,107,74,0,0,29.6,0.254,31,1
1,103,30,38,83,43.3,0.183,33,0
1,115,70,30,96,34.6,0.529,32,1
3,126,88,41,235,39.3,0.704,27,0
8,99,84,0,0,35.4,0.388,50,0
7,196,90,0,0,39.8,0.451,41,1
9,119,80,35,0,29.0,0.263,29,1
11,143,94,33,146,36.6,0.254,51,1
10,125,70,26,115,31.1,0.205,41,1
7,147,76,0,0,39.4,0.257,43,1
1,97,66,15,140,23.2,0.487,22,0
13,145,82,19,110,22.2,0.245,57,0
5,117,92,0,0,34.1,0.337,38,0
5,109,75,26,0,36.0,0.546,60,0
3,158,76,36,245,31.6,0.851,28,1
3,88,58,11,54,24.8,0.267,22,0
6,92,92,0,0,19.9,0.188,28,0
10,122,78,31,0,27.6,0.512,45,0
4,103,60,33,192,24.0,0.966,33,0
11,138,76,0,0,33.2,0.420,35,0
9,102,76,37,0,32.9,0.665,46,1
2,90,68,42,0,38.2,0.503,27,1
4,111,72,47,207,37.1,1.390,56,1
3,180,64,25,70,34.0,0.271,26,0
7,133,84,0,0,40.2,0.696,37,0
7,106,92,18,0,22.7,0.235,48,0
9,171,110,24,240,45.4,0.721,54,1
7,159,64,0,0,27.4,0.294,40,0
0,180,66,39,0,42.0,1.893,25,1
1,146,56,0,0,29.7,0.564,29,0
2,71,70,27,0,28.0,0.586,22,0
7,103,66,32,0,39.1,0.344,31,1
7,105,0,0,0,0.0,0.305,24,0
1,103,80,11,82,19.4,0.491,22,0
1,101,50,15,36,24.2,0.526,26,0
5,88,66,21,23,24.4,0.342,30,0
8,176,90,34,300,33.7,0.467,58,1
7,150,66,42,342,34.7,0.718,42,0
1,73,50,10,0,23.0,0.248,21,0
7,187,68,39,304,37.7,0.254,41,1
0,100,88,60,110,46.8,0.962,31,0
0,146,82,0,0,40.5,1.781,44,0
0,105,64,41,142,41.5,0.173,22,0
2,84,0,0,0,0.0,0.304,21,0
8,133,72,0,0,32.9,0.270,39,1
5,44,62,0,0,25.0,0.587,36,0
2,141,58,34,128,25.4,0.699,24,0
7,114,66,0,0,32.8,0.258,42,1
5,99,74,27,0,29.0,0.203,32,0
0,109,88,30,0,32.5,0.855,38,1
2,109,92,0,0,42.7,0.845,54,0
1,95,66,13,38,19.6,0.334,25,0
4,146,85,27,100,28.9,0.189,27,0
2,100,66,20,90,32.9,0.867,28,1
5,139,64,35,140,28.6,0.411,26,0
13,126,90,0,0,43.4,0.583,42,1
4,129,86,20,270,35.1,0.231,23,0
1,79,75,30,0,32.0,0.396,22,0
1,0,48,20,0,24.7,0.140,22,0
7,62,78,0,0,32.6,0.391,41,0
5,95,72,33,0,37.7,0.370,27,0
0,131,0,0,0,43.2,0.270,26,1
2,112,66,22,0,25.0,0.307,24,0
3,113,44,13,0,22.4,0.140,22,0
2,74,0,0,0,0.0,0.102,22,0
7,83,78,26,71,29.3,0.767,36,0
0,101,65,28,0,24.6,0.237,22,0
5,137,108,0,0,48.8,0.227,37,1
2,110,74,29,125,32.4,0.698,27,0
13,106,72,54,0,36.6,0.178,45,0
2,100,68,25,71,38.5,0.324,26,0
15,136,70,32,110,37.1,0.153,43,1
1,107,68,19,0,26.5,0.165,24,0
1,80,55,0,0,19.1,0.258,21,0
4,123,80,15,176,32.0,0.443,34,0
7,81,78,40,48,46.7,0.261,42,0
4,134,72,0,0,23.8,0.277,60,1
2,142,82,18,64,24.7,0.761,21,0
6,144,72,27,228,33.9,0.255,40,0
2,92,62,28,0,31.6,0.130,24,0
1,71,48,18,76,20.4,0.323,22,0
6,93,50,30,64,28.7,0.356,23,0
1,122,90,51,220,49.7,0.325,31,1
1,163,72,0,0,39.0,1.222,33,1
1,151,60,0,0,26.1,0.179,22,0
0,125,96,0,0,22.5,0.262,21,0
1,81,72,18,40,26.6,0.283,24,0
2,85,65,0,0,39.6,0.930,27,0
1,126,56,29,152,28.7,0.801,21,0
1,96,122,0,0,22.4,0.207,27,0
4,144,58,28,140,29.5,0.287,37,0
3,83,58,31,18,34.3,0.336,25,0
0,95,85,25,36,37.4,0.247,24,1
3,171,72,33,135,33.3,0.199,24,1
8,155,62,26,495,34.0,0.543,46,1
1,89,76,34,37,31.2,0.192,23,0
4,76,62,0,0,34.0,0.391,25,0
7,160,54,32,175,30.5,0.588,39,1
4,146,92,0,0,31.2,0.539,61,1
5,124,74,0,0,34.0,0.220,38,1
5,78,48,0,0,33.7,0.654,25,0
4,97,60,23,0,28.2,0.443,22,0
4,99,76,15,51,23.2,0.223,21,0
0,162,76,56,100,53.2,0.759,25,1
6,111,64,39,0,34.2,0.260,24,0
2,107,74,30,100,33.6,0.404,23,0
5,132,80,0,0,26.8,0.186,69,0
0,113,76,0,0,33.3,0.278,23,1
1,88,30,42,99,55.0,0.496,26,1
3,120,70,30,135,42.9,0.452,30,0
1,118,58,36,94,33.3,0.261,23,0
1,117,88,24,145,34.5,0.403,40,1
0,105,84,0,0,27.9,0.741,62,1
4,173,70,14,168,29.7,0.361,33,1
9,122,56,0,0,33.3,1.114,33,1
3,170,64,37,225,34.5,0.356,30,1
8,84,74,31,0,38.3,0.457,39,0
2,96,68,13,49,21.1,0.647,26,0
2,125,60,20,140,33.8,0.088,31,0
0,100,70,26,50,30.8,0.597,21,0
0,93,60,25,92,28.7,0.532,22,0
0,129,80,0,0,31.2,0.703,29,0
5,105,72,29,325,36.9,0.159,28,0
3,128,78,0,0,21.1,0.268,55,0
5,106,82,30,0,39.5,0.286,38,0
2,108,52,26,63,32.5,0.318,22,0
10,108,66,0,0,32.4,0.272,42,1
4,154,62,31,284,32.8,0.237,23,0
0,102,75,23,0,0.0,0.572,21,0
9,57,80,37,0,32.8,0.096,41,0
2,106,64,35,119,30.5,1.400,34,0
5,147,78,0,0,33.7,0.218,65,0
2,90,70,17,0,27.3,0.085,22,0
1,136,74,50,204,37.4,0.399,24,0
4,114,65,0,0,21.9,0.432,37,0
9,156,86,28,155,34.3,1.189,42,1
1,153,82,42,485,40.6,0.687,23,0
8,188,78,0,0,47.9,0.137,43,1
7,152,88,44,0,50.0,0.337,36,1
2,99,52,15,94,24.6,0.637,21,0
1,109,56,21,135,25.2,0.833,23,0
2,88,74,19,53,29.0,0.229,22,0
17,163,72,41,114,40.9,0.817,47,1
4,151,90,38,0,29.7,0.294,36,0
7,102,74,40,105,37.2,0.204,45,0
0,114,80,34,285,44.2,0.167,27,0
2,100,64,23,0,29.7,0.368,21,0
0,131,88,0,0,31.6,0.743,32,1
6,104,74,18,156,29.9,0.722,41,1
3,148,66,25,0,32.5,0.256,22,0
4,120,68,0,0,29.6,0.709,34,0
4,110,66,0,0,31.9,0.471,29,0
3,111,90,12,78,28.4,0.495,29,0
6,102,82,0,0,30.8,0.180,36,1
6,134,70,23,130,35.4,0.542,29,1
2,87,0,23,0,28.9,0.773,25,0
1,79,60,42,48,43.5,0.678,23,0
2,75,64,24,55,29.7,0.370,33,0
8,179,72,42,130,32.7,0.719,36,1
6,85,78,0,0,31.2,0.382,42,0
0,129,110,46,130,67.1,0.319,26,1
5,143,78,0,0,45.0,0.190,47,0
5,130,82,0,0,39.1,0.956,37,1
6,87,80,0,0,23.2,0.084,32,0
0,119,64,18,92,34.9,0.725,23,0
1,0,74,20,23,27.7,0.299,21,0
5,73,60,0,0,26.8,0.268,27,0
4,141,74,0,0,27.6,0.244,40,0
7,194,68,28,0,35.9,0.745,41,1
8,181,68,36,495,30.1,0.615,60,1
1,128,98,41,58,32.0,1.321,33,1
8,109,76,39,114,27.9,0.640,31,1
5,139,80,35,160,31.6,0.361,25,1
3,111,62,0,0,22.6,0.142,21,0
9,123,70,44,94,33.1,0.374,40,0
7,159,66,0,0,30.4,0.383,36,1
11,135,0,0,0,52.3,0.578,40,1
8,85,55,20,0,24.4,0.136,42,0
5,158,84,41,210,39.4,0.395,29,1
1,105,58,0,0,24.3,0.187,21,0
3,107,62,13,48,22.9,0.678,23,1
4,109,64,44,99,34.8,0.905,26,1
4,148,60,27,318,30.9,0.150,29,1
0,113,80,16,0,31.0,0.874,21,0
1,138,82,0,0,40.1,0.236,28,0
0,108,68,20,0,27.3,0.787,32,0
2,99,70,16,44,20.4,0.235,27,0
6,103,72,32,190,37.7,0.324,55,0
5,111,72,28,0,23.9,0.407,27,0
8,196,76,29,280,37.5,0.605,57,1
5,162,104,0,0,37.7,0.151,52,1
1,96,64,27,87,33.2,0.289,21,0
7,184,84,33,0,35.5,0.355,41,1
2,81,60,22,0,27.7,0.290,25,0
0,147,85,54,0,42.8,0.375,24,0
7,179,95,31,0,34.2,0.164,60,0
0,140,65,26,130,42.6,0.431,24,1
9,112,82,32,175,34.2,0.260,36,1
12,151,70,40,271,41.8,0.742,38,1
5,109,62,41,129,35.8,0.514,25,1
6,125,68,30,120,30.0,0.464,32,0
5,85,74,22,0,29.0,1.224,32,1
5,112,66,0,0,37.8,0.261,41,1
0,177,60,29,478,34.6,1.072,21,1
2,158,90,0,0,31.6,0.805,66,1
7,119,0,0,0,25.2,0.209,37,0
7,142,60,33,190,28.8,0.687,61,0
1,100,66,15,56,23.6,0.666,26,0
1,87,78,27,32,34.6,0.101,22,0
0,101,76,0,0,35.7,0.198,26,0
3,162,52,38,0,37.2,0.652,24,1
4,197,70,39,744,36.7,2.329,31,0
0,117,80,31,53,45.2,0.089,24,0
4,142,86,0,0,44.0,0.645,22,1
6,134,80,37,370,46.2,0.238,46,1
1,79,80,25,37,25.4,0.583,22,0
4,122,68,0,0,35.0,0.394,29,0
3,74,68,28,45,29.7,0.293,23,0
4,171,72,0,0,43.6,0.479,26,1
7,181,84,21,192,35.9,0.586,51,1
0,179,90,27,0,44.1,0.686,23,1
9,164,84,21,0,30.8,0.831,32,1
0,104,76,0,0,18.4,0.582,27,0
1,91,64,24,0,29.2,0.192,21,0
4,91,70,32,88,33.1,0.446,22,0
3,139,54,0,0,25.6,0.402,22,1
6,119,50,22,176,27.1,1.318,33,1
2,146,76,35,194,38.2,0.329,29,0
9,184,85,15,0,30.0,1.213,49,1
10,122,68,0,0,31.2,0.258,41,0
0,165,90,33,680,52.3,0.427,23,0
9,124,70,33,402,35.4,0.282,34,0
1,111,86,19,0,30.1,0.143,23,0
9,106,52,0,0,31.2,0.380,42,0
2,129,84,0,0,28.0,0.284,27,0
2,90,80,14,55,24.4,0.249,24,0
0,86,68,32,0,35.8,0.238,25,0
12,92,62,7,258,27.6,0.926,44,1
1,113,64,35,0,33.6,0.543,21,1
3,111,56,39,0,30.1,0.557,30,0
2,114,68,22,0,28.7,0.092,25,0
1,193,50,16,375,25.9,0.655,24,0
11,155,76,28,150,33.3,1.353,51,1
3,191,68,15,130,30.9,0.299,34,0
3,141,0,0,0,30.0,0.761,27,1
4,95,70,32,0,32.1,0.612,24,0
3,142,80,15,0,32.4,0.200,63,0
4,123,62,0,0,32.0,0.226,35,1
5,96,74,18,67,33.6,0.997,43,0
0,138,0,0,0,36.3,0.933,25,1
2,128,64,42,0,40.0,1.101,24,0
0,102,52,0,0,25.1,0.078,21,0
2,146,0,0,0,27.5,0.240,28,1
10,101,86,37,0,45.6,1.136,38,1
2,108,62,32,56,25.2,0.128,21,0
3,122,78,0,0,23.0,0.254,40,0
1,71,78,50,45,33.2,0.422,21,0
13,106,70,0,0,34.2,0.251,52,0
2,100,70,52,57,40.5,0.677,25,0
7,106,60,24,0,26.5,0.296,29,1
0,104,64,23,116,27.8,0.454,23,0
5,114,74,0,0,24.9,0.744,57,0
2,108,62,10,278,25.3,0.881,22,0
0,146,70,0,0,37.9,0.334,28,1
10,129,76,28,122,35.9,0.280,39,0
7,133,88,15,155,32.4,0.262,37,0
7,161,86,0,0,30.4,0.165,47,1
2,108,80,0,0,27.0,0.259,52,1
7,136,74,26,135,26.0,0.647,51,0
5,155,84,44,545,38.7,0.619,34,0
1,119,86,39,220,45.6,0.808,29,1
4,96,56,17,49,20.8,0.340,26,0
5,108,72,43,75,36.1,0.263,33,0
0,78,88,29,40,36.9,0.434,21,0
0,107,62,30,74,36.6,0.757,25,1
2,128,78,37,182,43.3,1.224,31,1
1,128,48,45,194,40.5,0.613,24,1
0,161,50,0,0,21.9,0.254,65,0
6,151,62,31,120,35.5,0.692,28,0
2,146,70,38,360,28.0,0.337,29,1
0,126,84,29,215,30.7,0.520,24,0
14,100,78,25,184,36.6,0.412,46,1
8,112,72,0,0,23.6,0.840,58,0
0,167,0,0,0,32.3,0.839,30,1
2,144,58,33,135,31.6,0.422,25,1
5,77,82,41,42,35.8,0.156,35,0
5,115,98,0,0,52.9,0.209,28,1
3,150,76,0,0,21.0,0.207,37,0
2,120,76,37,105,39.7,0.215,29,0
10,161,68,23,132,25.5,0.326,47,1
0,137,68,14,148,24.8,0.143,21,0
0,128,68,19,180,30.5,1.391,25,1
2,124,68,28,205,32.9,0.875,30,1
6,80,66,30,0,26.2,0.313,41,0
0,106,70,37,148,39.4,0.605,22,0
2,155,74,17,96,26.6,0.433,27,1
3,113,50,10,85,29.5,0.626,25,0
7,109,80,31,0,35.9,1.127,43,1
2,112,68,22,94,34.1,0.315,26,0
3,99,80,11,64,19.3,0.284,30,0
3,182,74,0,0,30.5,0.345,29,1
3,115,66,39,140,38.1,0.150,28,0
6,194,78,0,0,23.5,0.129,59,1
4,129,60,12,231,27.5,0.527,31,0
3,112,74,30,0,31.6,0.197,25,1
0,124,70,20,0,27.4,0.254,36,1
13,152,90,33,29,26.8,0.731,43,1
2,112,75,32,0,35.7,0.148,21,0
1,157,72,21,168,25.6,0.123,24,0
1,122,64,32,156,35.1,0.692,30,1
10,179,70,0,0,35.1,0.200,37,0
2,102,86,36,120,45.5,0.127,23,1
6,105,70,32,68,30.8,0.122,37,0
8,118,72,19,0,23.1,1.476,46,0
2,87,58,16,52,32.7,0.166,25,0
1,180,0,0,0,43.3,0.282,41,1
12,106,80,0,0,23.6,0.137,44,0
1,95,60,18,58,23.9,0.260,22,0
0,165,76,43,255,47.9,0.259,26,0
0,117,0,0,0,33.8,0.932,44,0
5,115,76,0,0,31.2,0.343,44,1
9,152,78,34,171,34.2,0.893,33,1
7,178,84,0,0,39.9,0.331,41,1
1,130,70,13,105,25.9,0.472,22,0
1,95,74,21,73,25.9,0.673,36,0
1,0,68,35,0,32.0,0.389,22,0
5,122,86,0,0,34.7,0.290,33,0
8,95,72,0,0,36.8,0.485,57,0
8,126,88,36,108,38.5,0.349,49,0
1,139,46,19,83,28.7,0.654,22,0
3,116,0,0,0,23.5,0.187,23,0
3,99,62,19,74,21.8,0.279,26,0
5,0,80,32,0,41.0,0.346,37,1
4,92,80,0,0,42.2,0.237,29,0
4,137,84,0,0,31.2,0.252,30,0
3,61,82,28,0,34.4,0.243,46,0
1,90,62,12,43,27.2,0.580,24,0
3,90,78,0,0,42.7,0.559,21,0
9,165,88,0,0,30.4,0.302,49,1
1,125,50,40,167,33.3,0.962,28,1
13,129,0,30,0,39.9,0.569,44,1
12,88,74,40,54,35.3,0.378,48,0
1,196,76,36,249,36.5,0.875,29,1
5,189,64,33,325,31.2,0.583,29,1
5,158,70,0,0,29.8,0.207,63,0
5,103,108,37,0,39.2,0.305,65,0
4,146,78,0,0,38.5,0.520,67,1
4,147,74,25,293,34.9,0.385,30,0
5,99,54,28,83,34.0,0.499,30,0
6,124,72,0,0,27.6,0.368,29,1
0,101,64,17,0,21.0,0.252,21,0
3,81,86,16,66,27.5,0.306,22,0
1,133,102,28,140,32.8,0.234,45,1
3,173,82,48,465,38.4,2.137,25,1
0,118,64,23,89,0.0,1.731,21,0
0,84,64,22,66,35.8,0.545,21,0
2,105,58,40,94,34.9,0.225,25,0
2,122,52,43,158,36.2,0.816,28,0
12,140,82,43,325,39.2,0.528,58,1
0,98,82,15,84,25.2,0.299,22,0
1,87,60,37,75,37.2,0.509,22,0
4,156,75,0,0,48.3,0.238,32,1
0,93,100,39,72,43.4,1.021,35,0
1,107,72,30,82,30.8,0.821,24,0
0,105,68,22,0,20.0,0.236,22,0
1,109,60,8,182,25.4,0.947,21,0
1,90,62,18,59,25.1,1.268,25,0
1,125,70,24,110,24.3,0.221,25,0
1,119,54,13,50,22.3,0.205,24,0
5,116,74,29,0,32.3,0.660,35,1
8,105,100,36,0,43.3,0.239,45,1
5,144,82,26,285,32.0,0.452,58,1
3,100,68,23,81,31.6,0.949,28,0
1,100,66,29,196,32.0,0.444,42,0
5,166,76,0,0,45.7,0.340,27,1
1,131,64,14,415,23.7,0.389,21,0
4,116,72,12,87,22.1,0.463,37,0
4,158,78,0,0,32.9,0.803,31,1
2,127,58,24,275,27.7,1.600,25,0
3,96,56,34,115,24.7,0.944,39,0
0,131,66,40,0,34.3,0.196,22,1
3,82,70,0,0,21.1,0.389,25,0
3,193,70,31,0,34.9,0.241,25,1
4,95,64,0,0,32.0,0.161,31,1
6,137,61,0,0,24.2,0.151,55,0
5,136,84,41,88,35.0,0.286,35,1
9,72,78,25,0,31.6,0.280,38,0
5,168,64,0,0,32.9,0.135,41,1
2,123,48,32,165,42.1,0.520,26,0
4,115,72,0,0,28.9,0.376,46,1
0,101,62,0,0,21.9,0.336,25,0
8,197,74,0,0,25.9,1.191,39,1
1,172,68,49,579,42.4,0.702,28,1
6,102,90,39,0,35.7,0.674,28,0
1,112,72,30,176,34.4,0.528,25,0
1,143,84,23,310,42.4,1.076,22,0
1,143,74,22,61,26.2,0.256,21,0
0,138,60,35,167,34.6,0.534,21,1
3,173,84,33,474,35.7,0.258,22,1
1,97,68,21,0,27.2,1.095,22,0
4,144,82,32,0,38.5,0.554,37,1
1,83,68,0,0,18.2,0.624,27,0
3,129,64,29,115,26.4,0.219,28,1
1,119,88,41,170,45.3,0.507,26,0
2,94,68,18,76,26.0,0.561,21,0
0,102,64,46,78,40.6,0.496,21,0
2,115,64,22,0,30.8,0.421,21,0
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8,108,70,0,0,30.5,0.955,33,1
4,117,62,12,0,29.7,0.380,30,1
0,180,78,63,14,59.4,2.420,25,1
1,100,72,12,70,25.3,0.658,28,0
0,95,80,45,92,36.5,0.330,26,0
0,104,64,37,64,33.6,0.510,22,1
0,120,74,18,63,30.5,0.285,26,0
1,82,64,13,95,21.2,0.415,23,0
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14,175,62,30,0,33.6,0.212,38,1
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5,86,68,28,71,30.2,0.364,24,0
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6,0,68,41,0,39.0,0.727,41,1
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0,139,62,17,210,22.1,0.207,21,0
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2,91,62,0,0,27.3,0.525,22,0
3,99,54,19,86,25.6,0.154,24,0
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3,125,58,0,0,31.6,0.151,24,0
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1,88,78,29,76,32.0,0.365,29,0
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1,97,70,40,0,38.1,0.218,30,0
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11,103,68,40,0,46.2,0.126,42,0
11,85,74,0,0,30.1,0.300,35,0
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0,198,66,32,274,41.3,0.502,28,1
1,87,68,34,77,37.6,0.401,24,0
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0,91,80,0,0,32.4,0.601,27,0
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1,99,72,30,18,38.6,0.412,21,0
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4,154,72,29,126,31.3,0.338,37,0
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1,167,74,17,144,23.4,0.447,33,1
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2,155,52,27,540,38.7,0.240,25,1
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1,120,80,48,200,38.9,1.162,41,0
11,127,106,0,0,39.0,0.190,51,0
3,80,82,31,70,34.2,1.292,27,1
10,162,84,0,0,27.7,0.182,54,0
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1,112,80,45,132,34.8,0.217,24,0
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bt5/erp5_wendelin_examples_keras/SkinTemplateItem/portal_skins/erp5_wendelin_examples_keras/pima.csv.xml 0 → 100644
View file @ b15c7d46
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<value> <string>text/csv</string> </value>
</item>
<item>
<key> <string>precondition</string> </key>
<value> <string></string> </value>
</item>
<item>
<key> <string>title</string> </key>
<value> <string></string> </value>
</item>
</dictionary>
</pickle>
</record>
</ZopeData>
bt5/erp5_wendelin_examples_keras/bt/description 0 → 100644
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Primitive examples of Keras in wendelin.
https://keras.io/
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bt5/erp5_wendelin_examples_keras/bt/template_extension_id_list 0 → 100644
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keras_save_load
keras_train_model
keras_vgg16_predict
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bt5/erp5_wendelin_examples_keras/bt/template_skin_id_list 0 → 100644
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erp5_wendelin_examples_keras
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bt5/erp5_wendelin_examples_keras/bt/title 0 → 100644
View file @ b15c7d46
erp5_wendelin_examples_keras
\ No newline at end of file
bt5/erp5_wendelin_examples_keras/bt/version 0 → 100644
View file @ b15c7d46
0.1
\ No newline at end of file
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