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using python 3.5.2 tensorflow rc 1.1
I'm trying to use a tensorflow metric function in keras. the required function interface seems to be the same, but calling:
import pandas
import numpy
import tensorflow.contrib.keras as keras
import tensorflow
def target_function(row):
return float(row[0] - row[1] < 0.5)
df = pandas.DataFrame(numpy.random.rand(10000,2))
label = df.apply(target_function, axis=1)
input_layer = keras.layers.Input(shape=(2,))
net = keras.layers.Dense(1)(input_layer)
model = keras.models.Model(inputs=[input_layer], outputs=[net])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[tensorflow.metrics.auc])
model.fit(df.as_matrix(), label.as_matrix(), epochs=10, validation_split=0.2, batch_size=100)
results with the error:
Using TensorFlow backend.
Traceback (most recent call last):
File "/Users/ophir/dev/ophir/tf_keras_metrics.py", line 49, in <module>
metrics=[precision, recall, tensorflow.metrics.auc]
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/keras/engine/training.py", line 956, in compile
metric_result = masked_metric_fn(y_true, y_pred, mask=masks[i])
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/keras/engine/training.py", line 489, in masked
return K.mean(score_array)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 1120, in mean
axis = _normalize_axis(axis, ndim(x))
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 437, in ndim
dims = x.get_shape()._dims
AttributeError: 'tuple' object has no attribute 'get_shape'
Process finished with exit code 1
EDIT
After incoporating the suggestion from Marcin Możejko
The code is:
import pandas
import numpy
import tensorflow.contrib.keras as keras
import tensorflow
def metric_function(y_true, y_pred):
return tensorflow.metrics.precision(y_true,y_pred)[0]
def target_function(row):
return float(row[0] - row[1] < 0.5)
df = pandas.DataFrame(numpy.random.rand(10000,2))
label = df.apply(target_function, axis=1)
input_layer = keras.layers.Input(shape=(2,))
net = keras.layers.Dense(1)(input_layer)
model = keras.models.Model(inputs=[input_layer], outputs=[net])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[metric_function])
model.fit(df.as_matrix(), label.as_matrix(), epochs=10, validation_split=0.2, batch_size=100)
and the error is:
/Users/ophir/anaconda3/envs/p3/bin/python /Users/ophir/dev/ophir/tf_keras_metrics.py
Train on 8000 samples, validate on 2000 samples
Epoch 1/10
2017-04-04 16:05:30.959006: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-04-04 16:05:30.959022: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-04-04 16:05:30.959026: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-04-04 16:05:30.959031: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-04-04 16:05:31.124262: W tensorflow/core/framework/op_kernel.cc:1152] Failed precondition: Attempting to use uninitialized value precision/true_positives/count
[[Node: precision/true_positives/count/read = Identity[T=DT_FLOAT, _class=["loc:@precision/true_positives/count"], _device="/job:localhost/replica:0/task:0/cpu:0"](precision/true_positives/count)]]
Traceback (most recent call last):
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1051, in _do_call
return fn(*args)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1033, in _run_fn
status, run_metadata)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value precision/true_positives/count
[[Node: precision/true_positives/count/read = Identity[T=DT_FLOAT, _class=["loc:@precision/true_positives/count"], _device="/job:localhost/replica:0/task:0/cpu:0"](precision/true_positives/count)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/ophir/dev/ophir/tf_keras_metrics.py", line 23, in <module>
model.fit(df.as_matrix(), label.as_matrix(), epochs=10, validation_split=0.2, batch_size=100)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/contrib/keras/python/keras/engine/training.py", line 1494, in fit
initial_epoch=initial_epoch)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/contrib/keras/python/keras/engine/training.py", line 1138, in _fit_loop
outs = f(ins_batch)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/contrib/keras/python/keras/backend.py", line 2245, in __call__
updated = session.run(self.outputs + [self.updates_op], feed_dict=feed_dict)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 786, in run
run_metadata_ptr)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 994, in _run
feed_dict_string, options, run_metadata)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1044, in _do_run
target_list, options, run_metadata)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1064, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value precision/true_positives/count
[[Node: precision/true_positives/count/read = Identity[T=DT_FLOAT, _class=["loc:@precision/true_positives/count"], _device="/job:localhost/replica:0/task:0/cpu:0"](precision/true_positives/count)]]
Caused by op 'precision/true_positives/count/read', defined at:
File "/Users/ophir/dev/ophir/tf_keras_metrics.py", line 21, in <module>
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[metric_function])
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/contrib/keras/python/keras/engine/training.py", line 958, in compile
metric_result = masked_metric_fn(y_true, y_pred, mask=masks[i])
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/contrib/keras/python/keras/engine/training.py", line 487, in masked
score_array = fn(y_true, y_pred)
File "/Users/ophir/dev/ophir/tf_keras_metrics.py", line 8, in metric_function
return tensorflow.metrics.precision(y_true,y_pred)[0]
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/metrics_impl.py", line 1377, in precision
updates_collections=None, name=None)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/metrics_impl.py", line 1274, in true_positives
updates_collections)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/metrics_impl.py", line 1211, in _count_condition
count = _create_local('count', shape=[])
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/metrics_impl.py", line 197, in _create_local
validate_shape=validate_shape)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/variables.py", line 197, in __init__
expected_shape=expected_shape)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/variables.py", line 316, in _init_from_args
self._snapshot = array_ops.identity(self._variable, name="read")
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1343, in identity
result = _op_def_lib.apply_op("Identity", input=input, name=name)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 768, in apply_op
op_def=op_def)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1228, in __init__
self._traceback = _extract_stack()
FailedPreconditionError (see above for traceback): Attempting to use uninitialized value precision/true_positives/count
[[Node: precision/true_positives/count/read = Identity[T=DT_FLOAT, _class=["loc:@precision/true_positives/count"], _device="/job:localhost/replica:0/task:0/cpu:0"](precision/true_positives/count)]]
Exception ignored in: <bound method BaseSession.__del__ of <tensorflow.python.client.session.Session object at 0x1140626d8>>
Traceback (most recent call last):
File "/Users/ophir/anaconda3/envs/p3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 595, in __del__
AttributeError: 'NoneType' object has no attribute 'TF_NewStatus'
Process finished with exit code 1
335
Thus, you can place your TensorFlow code directly into the Keras training pipeline or model. At the end of the day, use TensorFlow machine learning applications and Keras for deep neural networks.
Ok - I think I found an error. This funtion returns a tuple with (auc, other stuff). You need to overcome this by defying your own auc by:
def auc_tf_metric(y_true, y_pred):
return tensorflow.metrics.auc(y_true, y_pred)[0]
write by K.switch import keras.backend as K
def k_mean_iou(NUM_CLASSES):
"""
assume 0 is background for labels and prediction
labels,prediction with shape of [batch,height,width,class_number]
"""
def switch_mean_iou(labels, predictions):
mean_iou = K.variable(0.0)
seen_classes = K.variable(0.0)
for c in range(1,NUM_CLASSES):
labels_c = K.cast(K.equal(labels, c), K.floatx())
pred_c = K.cast(K.equal(predictions, c), K.floatx())
labels_c_sum = K.sum(labels_c)
pred_c_sum = K.sum(pred_c)
intersect = K.sum(labels_c*pred_c)
union = labels_c_sum + pred_c_sum - intersect
iou = intersect / union
condition = K.equal(union, 0)
mean_iou = K.switch(condition,
mean_iou,
mean_iou+iou)
seen_classes = K.switch(condition,
seen_classes,
seen_classes+1)
mean_iou = K.switch(K.equal(seen_classes, 0),
mean_iou,
mean_iou/seen_classes)
return mean_iou
return switch_mean_iou
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=k_mean_iou(class_number))
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