def feed_dict_builder(X, Y, net_inputs, net_targets):
""" Format provided data to a dictionary format compatible with
Tensorflow data feeding. It match all X and Y data provided with
net_inputs and net_targets provided placeholders. In case of inputs
data list, matching is made respectively.
Examples:
```python
# Building feed dictionary
>> feed_dict = feed_dict_builder(X, Y, input1, output1)
>> {input1: X, output1: Y}
>> feed_dict = feed_dict_builder({input1: X}, Y, input1, output1)
>> {input1: X, output1: Y}
>> feed_dict = feed_dict_builder([X1, X2], Y, [in1, in2], out1)
>> {in1: X1, in2: X2, output1: Y}
# For validation split:
>> val_feed_dict = feed_dict_builder(0.1, 0.1, input1, output1)
>> {input1: 0.1, output1: 0.1}
```
Arguments:
X: `array` or `dict`. The input data.
Y: `array`, `dict` or `float`. The targets (labels).
net_inputs: `list`. The network data inputs `Placeholders`.
net_targets: `list`. The network targets `Placeholders`.
Returns:
`dict`. A Tensorflow-ready dictionary to feed data.
Raises:
Exception if X and net_inputs or Y and net_targets list length doesn't
match.
"""
feed_dict = {}
if not (is_none(X) or is_none(net_inputs)):
# If input data are not a dict, we match them by creation order
if not isinstance(X, dict):
# If validation split, copy that value to the whole placeholders
if isinstance(X, float):
X = [X for _i in net_inputs]
elif len(net_inputs) > 1:
try: #TODO: Fix brodcast issue if different
if np.ndim(X) < 2:
raise ValueError("Multiple inputs but only one data "
"feeded. Please verify number of "
"inputs and data provided match.")
elif len(X) != len(net_inputs):
raise Exception(
str(len(X)) + " inputs feeded, "
"but expected: " + str(len(net_inputs)) +
". If you are using notebooks, please "
"make sure that you didn't run graph "
"construction cell multiple time, "
"or try to enclose your graph within "
"`with tf.Graph().as_default():` or "
"use `tf.reset_default_graph()`")
except Exception:
# Skip verif
pass
else:
X = [X]
for i, x in enumerate(X):
feed_dict[net_inputs[i]] = x
else:
# If a dict is provided
for key, val in X.items():
# Do nothing if dict already fits {placeholder: data} template
if isinstance(key, tf.Tensor):
continue
else: # Else retrieve placeholder with its name
var = vs.get_inputs_placeholder_by_name(key)
if var is None:
raise Exception(
"Feed dict asks for variable named '%s' but no "
"such variable is known to exist" % key)
feed_dict[var] = val
if not (is_none(Y) or is_none(net_targets)):
if not isinstance(Y, dict):
# Verify network has targets
if len(net_targets) == 0:
return feed_dict
# If validation split, copy that value to every target placeholder.
if isinstance(Y, float):
Y = [Y for _t in net_targets]
elif len(net_targets) > 1:
try: #TODO: Fix brodcast issue if different
if np.ndim(Y) < 2:
raise ValueError(
"Multiple outputs but only one data "
"feeded. Please verify number of outputs "
"and data provided match.")
elif len(Y) != len(net_targets):
raise Exception(
str(len(Y)) + " outputs feeded, "
"but expected: " + str(len(net_targets)))
except Exception:
# skip verif
pass
else:
Y = [Y]
for i, y in enumerate(Y):
feed_dict[net_targets[i]] = y
else:
# If a dict is provided
for key, val in Y.items():
# Do nothing if dict already fits {placeholder: data} template
if isinstance(key, tf.Tensor):
continue
else: # Else retrieve placeholder with its name
var = vs.get_targets_placeholder_by_name(key)
if var is None:
raise Exception(
"Feed dict asks for variable named '%s' but no "
"such variable is known to exist" % key)
feed_dict[var] = val
return feed_dict
def feed_dict_builder(X, Y, net_inputs, net_targets):
""" Format provided data to a dictionary format compatible with
Tensorflow data feeding. It match all X and Y data provided with
net_inputs and net_targets provided placeholders. In case of inputs
data list, matching is made respectively.
Examples:
```python
# Building feed dictionary
>> feed_dict = feed_dict_builder(X, Y, input1, output1)
>> {input1: X, output1: Y}
>> feed_dict = feed_dict_builder({input1: X}, Y, input1, output1)
>> {input1: X, output1: Y}
>> feed_dict = feed_dict_builder([X1, X2], Y, [in1, in2], out1)
>> {in1: X1, in2: X2, output1: Y}
# For validation split:
>> val_feed_dict = feed_dict_builder(0.1, 0.1, input1, output1)
>> {input1: 0.1, output1: 0.1}
```
Arguments:
X: `array` or `dict`. The input data.
Y: `array`, `dict` or `float`. The targets (labels).
net_inputs: `list`. The network data inputs `Placeholders`.
net_targets: `list`. The network targets `Placeholders`.
Returns:
`dict`. A Tensorflow-ready dictionary to feed data.
Raises:
Exception if X and net_inputs or Y and net_targets list length doesn't
match.
"""
feed_dict = {}
if not (is_none(X) or is_none(net_inputs)):
# If input data are not a dict, we match them by creation order
if not isinstance(X, dict):
# If validation split, copy that value to the whole placeholders
if isinstance(X, float): X = [X for _i in net_inputs]
elif len(net_inputs) > 1:
if np.ndim(X) < 2:
raise ValueError("Multiple inputs but only one data "
"feeded. Please verify number of inputs "
"and data provided match.")
elif len(X) != len(net_inputs):
raise Exception(str(len(X)) + " inputs feeded, "
"but expected: " + str(len(net_inputs)) +
". If you are using notebooks, please "
"make sure that you didn't run graph "
"construction cell multiple time, "
"or try to enclose your graph within "
"`with tf.Graph().as_default():`")
else:
X = [X]
for i, x in enumerate(X):
feed_dict[net_inputs[i]] = x
else:
# If a dict is provided
for key, val in X.items():
# Do nothing if dict already fits {placeholder: data} template
if isinstance(key, tf.Tensor):
continue
else: # Else retrieve placeholder with its name
feed_dict[vs.get_inputs_placeholder_by_name(key)] = val
if not (is_none(Y) or is_none(net_targets)):
if not isinstance(Y, dict):
# Verify network has targets
if len(net_targets) == 0:
return feed_dict
# If validation split, copy that value to every target placeholder.
if isinstance(Y, float):
Y = [Y for _t in net_targets]
elif len(net_targets) > 1:
if np.ndim(Y) < 2:
raise ValueError("Multiple outputs but only one data "
"feeded. Please verify number of outputs "
"and data provided match.")
elif len(Y) != len(net_targets):
raise Exception(str(len(Y)) + " outputs feeded, "
"but expected: " + str(len(net_targets)))
else: Y = [Y]
for i, y in enumerate(Y):
feed_dict[net_targets[i]] = y
else:
# If a dict is provided
for key, val in Y.items():
# Do nothing if dict already fits {placeholder: data} template
if isinstance(key, tf.Tensor):
continue
else: # Else retrieve placeholder with its name
feed_dict[vs.get_targets_placeholder_by_name(key)] = val
return feed_dict
def feed_dict_builder(X, Y, net_inputs, net_targets):
""" Format provided data to a dictionary format compatible with
Tensorflow data feeding. It match all X and Y data provided with
net_inputs and net_targets provided placeholders. In case of inputs
data list, matching is made respectively.
Examples:
```python
# Building feed dictionary
>> feed_dict = feed_dict_builder(X, Y, input1, output1)
>> {input1: X, output1: Y}
>> feed_dict = feed_dict_builder({input1: X}, Y, input1, output1)
>> {input1: X, output1: Y}
>> feed_dict = feed_dict_builder([X1, X2], Y, [in1, in2], out1)
>> {in1: X1, in2: X2, output1: Y}
# For validation split:
>> val_feed_dict = feed_dict_builder(0.1, 0.1, input1, output1)
>> {input1: 0.1, output1: 0.1}
```
Arguments:
X: `array` or `dict`. The input data.
Y: `array`, `dict` or `float`. The targets (labels).
net_inputs: `list`. The network data inputs `Placeholders`.
net_targets: `list`. The network targets `Placeholders`.
Returns:
`dict`. A Tensorflow-ready dictionary to feed data.
Raises:
Exception if X and net_inputs or Y and net_targets list length doesn't
match.
"""
feed_dict = {}
if not (is_none(X) or is_none(net_inputs)):
# If input data are not a dict, we match them by creation order
if not isinstance(X, dict):
# If validation split, copy that value to the whole placeholders
if isinstance(X, float): X = [X for _i in net_inputs]
elif len(net_inputs) > 1:
if np.ndim(X) < 2:
raise ValueError("Multiple inputs but only one data "
"feeded. Please verify number of inputs "
"and data provided match.")
elif len(X) != len(net_inputs):
raise Exception(
str(len(X)) + " inputs feeded, "
"but expected: " + str(len(net_inputs)))
else:
X = [X]
for i, x in enumerate(X):
feed_dict[net_inputs[i]] = x
else:
# If a dict is provided
for key, val in X.items():
# Do nothing if dict already fits {placeholder: data} template
if isinstance(key, tf.Tensor):
continue
else: # Else retrieve placeholder with its name
feed_dict[vs.get_inputs_placeholder_by_name(key)] = val
if not (is_none(Y) or is_none(net_targets)):
if not isinstance(Y, dict):
# Verify network has targets
if len(net_targets) == 0:
return feed_dict
# If validation split, copy that value to every target placeholder.
if isinstance(Y, float):
Y = [Y for _t in net_targets]
elif len(net_targets) > 1:
if np.ndim(Y) < 2:
raise ValueError("Multiple outputs but only one data "
"feeded. Please verify number of outputs "
"and data provided match.")
elif len(Y) != len(net_targets):
raise Exception(
str(len(Y)) + " outputs feeded, "
"but expected: " + str(len(net_targets)))
else:
Y = [Y]
for i, y in enumerate(Y):
feed_dict[net_targets[i]] = y
else:
# If a dict is provided
for key, val in Y.items():
# Do nothing if dict already fits {placeholder: data} template
if isinstance(key, tf.Tensor):
continue
else: # Else retrieve placeholder with its name
feed_dict[vs.get_targets_placeholder_by_name(key)] = val
return feed_dict