Commit 113ea42c authored by Yusei Tahara's avatar Yusei Tahara

Revert "erp5_wendelin_examples_keras: Add primitive examples of Keras in wendelin."

This reverts commit fc67555b.
parent fc67555b
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
<?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>
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
<?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>
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<pickle>
<global name="PersistentMapping" module="Persistence.mapping"/>
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<pickle>
<dictionary>
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<key> <string>data</string> </key>
<value>
<dictionary/>
</value>
</item>
</dictionary>
</pickle>
</record>
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<pickle>
<global name="PersistentMapping" module="Persistence.mapping"/>
</pickle>
<pickle>
<dictionary>
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<key> <string>data</string> </key>
<value>
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<key> <string>component_validation_workflow</string> </key>
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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
<?xml version="1.0"?>
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<key> <string>component_validation_workflow</string> </key>
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<value> <string>validated</string> </value>
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</tuple>
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<ZopeData>
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<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>
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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.
<?xml version="1.0"?>
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<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>
for i in context.keras_vgg16_predict(context.image_module[image_document_id]):
print i
return printed
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</item>
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</state>
</object>
</value>
</item>
<item>
<key> <string>_params</string> </key>
<value> <string>image_document_id</string> </value>
</item>
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<value> <string>context</string> </value>
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<key> <string>_params</string> </key>
<value> <string></string> </value>
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<key> <string>id</string> </key>
<value> <string>call_read_keras_log</string> </value>
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<value> <string>script</string> </value>
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<value> <string>traverse_subpath</string> </value>
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<key> <string>_params</string> </key>
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<value> <string>call_train_keras</string> </value>
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<value> <string>load_model</string> </value>
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<value> <string>keras_save_load</string> </value>
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<item>
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<value> <string>keras_load_model</string> </value>
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<value> <string>train</string> </value>
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<value> <string>keras_train_model</string> </value>
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<item>
<key> <string>id</string> </key>
<value> <string>keras_train_model</string> </value>
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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
8,151,78,32,210,42.9,0.516,36,1
4,184,78,39,277,37.0,0.264,31,1
0,94,0,0,0,0.0,0.256,25,0
1,181,64,30,180,34.1,0.328,38,1
0,135,94,46,145,40.6,0.284,26,0
1,95,82,25,180,35.0,0.233,43,1
2,99,0,0,0,22.2,0.108,23,0
3,89,74,16,85,30.4,0.551,38,0
1,80,74,11,60,30.0,0.527,22,0
2,139,75,0,0,25.6,0.167,29,0
1,90,68,8,0,24.5,1.138,36,0
0,141,0,0,0,42.4,0.205,29,1
12,140,85,33,0,37.4,0.244,41,0
5,147,75,0,0,29.9,0.434,28,0
1,97,70,15,0,18.2,0.147,21,0
6,107,88,0,0,36.8,0.727,31,0
0,189,104,25,0,34.3,0.435,41,1
2,83,66,23,50,32.2,0.497,22,0
4,117,64,27,120,33.2,0.230,24,0
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
2,134,70,0,0,28.9,0.542,23,1
0,91,68,32,210,39.9,0.381,25,0
2,119,0,0,0,19.6,0.832,72,0
2,100,54,28,105,37.8,0.498,24,0
14,175,62,30,0,33.6,0.212,38,1
1,135,54,0,0,26.7,0.687,62,0
5,86,68,28,71,30.2,0.364,24,0
10,148,84,48,237,37.6,1.001,51,1
9,134,74,33,60,25.9,0.460,81,0
9,120,72,22,56,20.8,0.733,48,0
1,71,62,0,0,21.8,0.416,26,0
8,74,70,40,49,35.3,0.705,39,0
5,88,78,30,0,27.6,0.258,37,0
10,115,98,0,0,24.0,1.022,34,0
0,124,56,13,105,21.8,0.452,21,0
0,74,52,10,36,27.8,0.269,22,0
0,97,64,36,100,36.8,0.600,25,0
8,120,0,0,0,30.0,0.183,38,1
6,154,78,41,140,46.1,0.571,27,0
1,144,82,40,0,41.3,0.607,28,0
0,137,70,38,0,33.2,0.170,22,0
0,119,66,27,0,38.8,0.259,22,0
7,136,90,0,0,29.9,0.210,50,0
4,114,64,0,0,28.9,0.126,24,0
0,137,84,27,0,27.3,0.231,59,0
2,105,80,45,191,33.7,0.711,29,1
7,114,76,17,110,23.8,0.466,31,0
8,126,74,38,75,25.9,0.162,39,0
4,132,86,31,0,28.0,0.419,63,0
3,158,70,30,328,35.5,0.344,35,1
0,123,88,37,0,35.2,0.197,29,0
4,85,58,22,49,27.8,0.306,28,0
0,84,82,31,125,38.2,0.233,23,0
0,145,0,0,0,44.2,0.630,31,1
0,135,68,42,250,42.3,0.365,24,1
1,139,62,41,480,40.7,0.536,21,0
0,173,78,32,265,46.5,1.159,58,0
4,99,72,17,0,25.6,0.294,28,0
8,194,80,0,0,26.1,0.551,67,0
2,83,65,28,66,36.8,0.629,24,0
2,89,90,30,0,33.5,0.292,42,0
4,99,68,38,0,32.8,0.145,33,0
4,125,70,18,122,28.9,1.144,45,1
3,80,0,0,0,0.0,0.174,22,0
6,166,74,0,0,26.6,0.304,66,0
5,110,68,0,0,26.0,0.292,30,0
2,81,72,15,76,30.1,0.547,25,0
7,195,70,33,145,25.1,0.163,55,1
6,154,74,32,193,29.3,0.839,39,0
2,117,90,19,71,25.2,0.313,21,0
3,84,72,32,0,37.2,0.267,28,0
6,0,68,41,0,39.0,0.727,41,1
7,94,64,25,79,33.3,0.738,41,0
3,96,78,39,0,37.3,0.238,40,0
10,75,82,0,0,33.3,0.263,38,0
0,180,90,26,90,36.5,0.314,35,1
1,130,60,23,170,28.6,0.692,21,0
2,84,50,23,76,30.4,0.968,21,0
8,120,78,0,0,25.0,0.409,64,0
12,84,72,31,0,29.7,0.297,46,1
0,139,62,17,210,22.1,0.207,21,0
9,91,68,0,0,24.2,0.200,58,0
2,91,62,0,0,27.3,0.525,22,0
3,99,54,19,86,25.6,0.154,24,0
3,163,70,18,105,31.6,0.268,28,1
9,145,88,34,165,30.3,0.771,53,1
7,125,86,0,0,37.6,0.304,51,0
13,76,60,0,0,32.8,0.180,41,0
6,129,90,7,326,19.6,0.582,60,0
2,68,70,32,66,25.0,0.187,25,0
3,124,80,33,130,33.2,0.305,26,0
6,114,0,0,0,0.0,0.189,26,0
9,130,70,0,0,34.2,0.652,45,1
3,125,58,0,0,31.6,0.151,24,0
3,87,60,18,0,21.8,0.444,21,0
1,97,64,19,82,18.2,0.299,21,0
3,116,74,15,105,26.3,0.107,24,0
0,117,66,31,188,30.8,0.493,22,0
0,111,65,0,0,24.6,0.660,31,0
2,122,60,18,106,29.8,0.717,22,0
0,107,76,0,0,45.3,0.686,24,0
1,86,66,52,65,41.3,0.917,29,0
6,91,0,0,0,29.8,0.501,31,0
1,77,56,30,56,33.3,1.251,24,0
4,132,0,0,0,32.9,0.302,23,1
0,105,90,0,0,29.6,0.197,46,0
0,57,60,0,0,21.7,0.735,67,0
0,127,80,37,210,36.3,0.804,23,0
3,129,92,49,155,36.4,0.968,32,1
8,100,74,40,215,39.4,0.661,43,1
3,128,72,25,190,32.4,0.549,27,1
10,90,85,32,0,34.9,0.825,56,1
4,84,90,23,56,39.5,0.159,25,0
1,88,78,29,76,32.0,0.365,29,0
8,186,90,35,225,34.5,0.423,37,1
5,187,76,27,207,43.6,1.034,53,1
4,131,68,21,166,33.1,0.160,28,0
1,164,82,43,67,32.8,0.341,50,0
4,189,110,31,0,28.5,0.680,37,0
1,116,70,28,0,27.4,0.204,21,0
3,84,68,30,106,31.9,0.591,25,0
6,114,88,0,0,27.8,0.247,66,0
1,88,62,24,44,29.9,0.422,23,0
1,84,64,23,115,36.9,0.471,28,0
7,124,70,33,215,25.5,0.161,37,0
1,97,70,40,0,38.1,0.218,30,0
8,110,76,0,0,27.8,0.237,58,0
11,103,68,40,0,46.2,0.126,42,0
11,85,74,0,0,30.1,0.300,35,0
6,125,76,0,0,33.8,0.121,54,1
0,198,66,32,274,41.3,0.502,28,1
1,87,68,34,77,37.6,0.401,24,0
6,99,60,19,54,26.9,0.497,32,0
0,91,80,0,0,32.4,0.601,27,0
2,95,54,14,88,26.1,0.748,22,0
1,99,72,30,18,38.6,0.412,21,0
6,92,62,32,126,32.0,0.085,46,0
4,154,72,29,126,31.3,0.338,37,0
0,121,66,30,165,34.3,0.203,33,1
3,78,70,0,0,32.5,0.270,39,0
2,130,96,0,0,22.6,0.268,21,0
3,111,58,31,44,29.5,0.430,22,0
2,98,60,17,120,34.7,0.198,22,0
1,143,86,30,330,30.1,0.892,23,0
1,119,44,47,63,35.5,0.280,25,0
6,108,44,20,130,24.0,0.813,35,0
2,118,80,0,0,42.9,0.693,21,1
10,133,68,0,0,27.0,0.245,36,0
2,197,70,99,0,34.7,0.575,62,1
0,151,90,46,0,42.1,0.371,21,1
6,109,60,27,0,25.0,0.206,27,0
12,121,78,17,0,26.5,0.259,62,0
8,100,76,0,0,38.7,0.190,42,0
8,124,76,24,600,28.7,0.687,52,1
1,93,56,11,0,22.5,0.417,22,0
8,143,66,0,0,34.9,0.129,41,1
6,103,66,0,0,24.3,0.249,29,0
3,176,86,27,156,33.3,1.154,52,1
0,73,0,0,0,21.1,0.342,25,0
11,111,84,40,0,46.8,0.925,45,1
2,112,78,50,140,39.4,0.175,24,0
3,132,80,0,0,34.4,0.402,44,1
2,82,52,22,115,28.5,1.699,25,0
6,123,72,45,230,33.6,0.733,34,0
0,188,82,14,185,32.0,0.682,22,1
0,67,76,0,0,45.3,0.194,46,0
1,89,24,19,25,27.8,0.559,21,0
1,173,74,0,0,36.8,0.088,38,1
1,109,38,18,120,23.1,0.407,26,0
1,108,88,19,0,27.1,0.400,24,0
6,96,0,0,0,23.7,0.190,28,0
1,124,74,36,0,27.8,0.100,30,0
7,150,78,29,126,35.2,0.692,54,1
4,183,0,0,0,28.4,0.212,36,1
1,124,60,32,0,35.8,0.514,21,0
1,181,78,42,293,40.0,1.258,22,1
1,92,62,25,41,19.5,0.482,25,0
0,152,82,39,272,41.5,0.270,27,0
1,111,62,13,182,24.0,0.138,23,0
3,106,54,21,158,30.9,0.292,24,0
3,174,58,22,194,32.9,0.593,36,1
7,168,88,42,321,38.2,0.787,40,1
6,105,80,28,0,32.5,0.878,26,0
11,138,74,26,144,36.1,0.557,50,1
3,106,72,0,0,25.8,0.207,27,0
6,117,96,0,0,28.7,0.157,30,0
2,68,62,13,15,20.1,0.257,23,0
9,112,82,24,0,28.2,1.282,50,1
0,119,0,0,0,32.4,0.141,24,1
2,112,86,42,160,38.4,0.246,28,0
2,92,76,20,0,24.2,1.698,28,0
6,183,94,0,0,40.8,1.461,45,0
0,94,70,27,115,43.5,0.347,21,0
2,108,64,0,0,30.8,0.158,21,0
4,90,88,47,54,37.7,0.362,29,0
0,125,68,0,0,24.7,0.206,21,0
0,132,78,0,0,32.4,0.393,21,0
5,128,80,0,0,34.6,0.144,45,0
4,94,65,22,0,24.7,0.148,21,0
7,114,64,0,0,27.4,0.732,34,1
0,102,78,40,90,34.5,0.238,24,0
2,111,60,0,0,26.2,0.343,23,0
1,128,82,17,183,27.5,0.115,22,0
10,92,62,0,0,25.9,0.167,31,0
13,104,72,0,0,31.2,0.465,38,1
5,104,74,0,0,28.8,0.153,48,0
2,94,76,18,66,31.6,0.649,23,0
7,97,76,32,91,40.9,0.871,32,1
1,100,74,12,46,19.5,0.149,28,0
0,102,86,17,105,29.3,0.695,27,0
4,128,70,0,0,34.3,0.303,24,0
6,147,80,0,0,29.5,0.178,50,1
4,90,0,0,0,28.0,0.610,31,0
3,103,72,30,152,27.6,0.730,27,0
2,157,74,35,440,39.4,0.134,30,0
1,167,74,17,144,23.4,0.447,33,1
0,179,50,36,159,37.8,0.455,22,1
11,136,84,35,130,28.3,0.260,42,1
0,107,60,25,0,26.4,0.133,23,0
1,91,54,25,100,25.2,0.234,23,0
1,117,60,23,106,33.8,0.466,27,0
5,123,74,40,77,34.1,0.269,28,0
2,120,54,0,0,26.8,0.455,27,0
1,106,70,28,135,34.2,0.142,22,0
2,155,52,27,540,38.7,0.240,25,1
2,101,58,35,90,21.8,0.155,22,0
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
1,199,76,43,0,42.9,1.394,22,1
8,167,106,46,231,37.6,0.165,43,1
9,145,80,46,130,37.9,0.637,40,1
6,115,60,39,0,33.7,0.245,40,1
1,112,80,45,132,34.8,0.217,24,0
4,145,82,18,0,32.5,0.235,70,1
10,111,70,27,0,27.5,0.141,40,1
6,98,58,33,190,34.0,0.430,43,0
9,154,78,30,100,30.9,0.164,45,0
6,165,68,26,168,33.6,0.631,49,0
1,99,58,10,0,25.4,0.551,21,0
10,68,106,23,49,35.5,0.285,47,0
3,123,100,35,240,57.3,0.880,22,0
8,91,82,0,0,35.6,0.587,68,0
6,195,70,0,0,30.9,0.328,31,1
9,156,86,0,0,24.8,0.230,53,1
0,93,60,0,0,35.3,0.263,25,0
3,121,52,0,0,36.0,0.127,25,1
2,101,58,17,265,24.2,0.614,23,0
2,56,56,28,45,24.2,0.332,22,0
0,162,76,36,0,49.6,0.364,26,1
0,95,64,39,105,44.6,0.366,22,0
4,125,80,0,0,32.3,0.536,27,1
5,136,82,0,0,0.0,0.640,69,0
2,129,74,26,205,33.2,0.591,25,0
3,130,64,0,0,23.1,0.314,22,0
1,107,50,19,0,28.3,0.181,29,0
1,140,74,26,180,24.1,0.828,23,0
1,144,82,46,180,46.1,0.335,46,1
8,107,80,0,0,24.6,0.856,34,0
13,158,114,0,0,42.3,0.257,44,1
2,121,70,32,95,39.1,0.886,23,0
7,129,68,49,125,38.5,0.439,43,1
2,90,60,0,0,23.5,0.191,25,0
7,142,90,24,480,30.4,0.128,43,1
3,169,74,19,125,29.9,0.268,31,1
0,99,0,0,0,25.0,0.253,22,0
4,127,88,11,155,34.5,0.598,28,0
4,118,70,0,0,44.5,0.904,26,0
2,122,76,27,200,35.9,0.483,26,0
6,125,78,31,0,27.6,0.565,49,1
1,168,88,29,0,35.0,0.905,52,1
2,129,0,0,0,38.5,0.304,41,0
4,110,76,20,100,28.4,0.118,27,0
6,80,80,36,0,39.8,0.177,28,0
10,115,0,0,0,0.0,0.261,30,1
2,127,46,21,335,34.4,0.176,22,0
9,164,78,0,0,32.8,0.148,45,1
2,93,64,32,160,38.0,0.674,23,1
3,158,64,13,387,31.2,0.295,24,0
5,126,78,27,22,29.6,0.439,40,0
10,129,62,36,0,41.2,0.441,38,1
0,134,58,20,291,26.4,0.352,21,0
3,102,74,0,0,29.5,0.121,32,0
7,187,50,33,392,33.9,0.826,34,1
3,173,78,39,185,33.8,0.970,31,1
10,94,72,18,0,23.1,0.595,56,0
1,108,60,46,178,35.5,0.415,24,0
5,97,76,27,0,35.6,0.378,52,1
4,83,86,19,0,29.3,0.317,34,0
1,114,66,36,200,38.1,0.289,21,0
1,149,68,29,127,29.3,0.349,42,1
5,117,86,30,105,39.1,0.251,42,0
1,111,94,0,0,32.8,0.265,45,0
4,112,78,40,0,39.4,0.236,38,0
1,116,78,29,180,36.1,0.496,25,0
0,141,84,26,0,32.4,0.433,22,0
2,175,88,0,0,22.9,0.326,22,0
2,92,52,0,0,30.1,0.141,22,0
3,130,78,23,79,28.4,0.323,34,1
8,120,86,0,0,28.4,0.259,22,1
2,174,88,37,120,44.5,0.646,24,1
2,106,56,27,165,29.0,0.426,22,0
2,105,75,0,0,23.3,0.560,53,0
4,95,60,32,0,35.4,0.284,28,0
0,126,86,27,120,27.4,0.515,21,0
8,65,72,23,0,32.0,0.600,42,0
2,99,60,17,160,36.6,0.453,21,0
1,102,74,0,0,39.5,0.293,42,1
11,120,80,37,150,42.3,0.785,48,1
3,102,44,20,94,30.8,0.400,26,0
1,109,58,18,116,28.5,0.219,22,0
9,140,94,0,0,32.7,0.734,45,1
13,153,88,37,140,40.6,1.174,39,0
12,100,84,33,105,30.0,0.488,46,0
1,147,94,41,0,49.3,0.358,27,1
1,81,74,41,57,46.3,1.096,32,0
3,187,70,22,200,36.4,0.408,36,1
6,162,62,0,0,24.3,0.178,50,1
4,136,70,0,0,31.2,1.182,22,1
1,121,78,39,74,39.0,0.261,28,0
3,108,62,24,0,26.0,0.223,25,0
0,181,88,44,510,43.3,0.222,26,1
8,154,78,32,0,32.4,0.443,45,1
1,128,88,39,110,36.5,1.057,37,1
7,137,90,41,0,32.0,0.391,39,0
0,123,72,0,0,36.3,0.258,52,1
1,106,76,0,0,37.5,0.197,26,0
6,190,92,0,0,35.5,0.278,66,1
2,88,58,26,16,28.4,0.766,22,0
9,170,74,31,0,44.0,0.403,43,1
9,89,62,0,0,22.5,0.142,33,0
10,101,76,48,180,32.9,0.171,63,0
2,122,70,27,0,36.8,0.340,27,0
5,121,72,23,112,26.2,0.245,30,0
1,126,60,0,0,30.1,0.349,47,1
1,93,70,31,0,30.4,0.315,23,0
\ No newline at end of file
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<pickle>
<dictionary>
<item>
<key> <string>__name__</string> </key>
<value> <string>pima.csv</string> </value>
</item>
<item>
<key> <string>content_type</string> </key>
<value> <string>text/csv</string> </value>
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<item>
<key> <string>precondition</string> </key>
<value> <string></string> </value>
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Primitive examples of Keras in wendelin.
https://keras.io/
\ No newline at end of file
keras_save_load
keras_train_model
keras_vgg16_predict
\ No newline at end of file
erp5_wendelin_examples_keras
\ No newline at end of file
erp5_wendelin_examples_keras
\ No newline at end of file
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