运行模拟后，我收到此消息。我不明白这是问题所在。你能帮我吗？
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/linear.py:469: multi_class_head (from tensorflow.contrib.learn.python.learn.estimators.head) is deprecated and will be removed in a future version.
Instructions for updating:
Please switch to tf.contrib.estimator.*_head.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1180: __init__ (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.
Instructions for updating:
Please replace uses of any Estimator from tf.contrib.learn with an Estimator from tf.estimator.*
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:428: __init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.
Instructions for updating:
When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_task_type': None, '_train_distribute': None, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f9bbbfcd090>, '_tf_config': gpu_options {
per_process_gpu_memory_fraction: 1.0
}
, '_protocol': None, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_session_config': None, '_model_dir': '/tmp/tmpzv3Q2e', '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 0, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_environment': 'local', '_save_summary_steps': 100}
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column.py:2391: calling sparse_feature_cross (from tensorflow.contrib.layers.python.ops.sparse_feature_cross_op) with hash_key=None is deprecated and will be removed after 2016-11-20.
Instructions for updating:
The default behavior of sparse_feature_cross is changing, the default
value for hash_key will change to SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY.
From that point on sparse_feature_cross will always use FingerprintCat64
to concatenate the feature fingerprints. And the underlying
_sparse_feature_cross_op.sparse_feature_cross operation will be marked
as deprecated.
WARNING:tensorflow:Casting <dtype: 'int64'> labels to bool.
WARNING:tensorflow:Casting <dtype: 'int64'> labels to bool.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: __new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.
Instructions for updating:
When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
2018-12-04 15:12:52.326004: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpzv3Q2e/model.ckpt.
2018-12-04 15:13:02.209850: W tensorflow/core/framework/op_kernel.cc:1250] OP_REQUIRES failed at cast_op.cc:123 : Unimplemented: Cast string to float is not supported
2018-12-04 15:13:02.209963: E tensorflow/core/common_runtime/executor.cc:623] Executor failed to create kernel. Unimplemented: Cast string to float is not supported
[[{{node linear/linear/ToFloat_9}} = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, Truncate=false, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Const_10)]]
2018-12-04 15:13:02.229424: W tensorflow/core/framework/op_kernel.cc:1250] OP_REQUIRES failed at cast_op.cc:123 : Unimplemented: Cast string to float is not supported
2018-12-04 15:13:02.229522: E tensorflow/core/common_runtime/executor.cc:623] Executor failed to create kernel. Unimplemented: Cast string to float is not supported
[[{{node linear/linear/ToFloat}} = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, Truncate=false, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Const_1)]]
Traceback (most recent call last):
File "linear1.py", line 87, in <module>
m.fit(input_fn=train_input_fn, steps=2000)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 525, in fit
loss = self._train_model(input_fn=input_fn, hooks=hooks)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1095, in _train_model
_, loss = mon_sess.run([model_fn_ops.train_op, model_fn_ops.loss])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 671, in run
run_metadata=run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 1156, in run
run_metadata=run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 1255, in run
raise six.reraise(*original_exc_info)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 1240, in run
return self._sess.run(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 1312, in run
run_metadata=run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py", line 1076, in run
return self._sess.run(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 929, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1152, in _run
feed_dict_tensor, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1328, in _do_run
run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1348, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.UnimplementedError: Cast string to float is not supported
[[node linear/linear/ToFloat (defined at /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column.py:1875) = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, Truncate=false, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Const_1)]]
Caused by op u'linear/linear/ToFloat', defined at:
File "linear1.py", line 87, in <module>
m.fit(input_fn=train_input_fn, steps=2000)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 525, in fit
loss = self._train_model(input_fn=input_fn, hooks=hooks)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1042, in _train_model
model_fn_ops = self._get_train_ops(features, labels)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1265, in _get_train_ops
return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.TRAIN)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1228, in _call_model_fn
model_fn_results = self._model_fn(features, labels, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/linear.py", line 169, in _linear_model_fn
scope=scope)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.py", line 495, in weighted_sum_from_feature_columns
transformed_tensor = transformer.transform(column)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column_ops.py", line 848, in transform
feature_column.insert_transformed_feature(self._columns_to_tensors)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column.py", line 1875, in insert_transformed_feature
columns_to_tensors[self] = math_ops.to_float(input_tensor)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 732, in to_float
return cast(x, dtypes.float32, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 677, in cast
x = gen_math_ops.cast(x, base_type, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 1659, in cast
"Cast", x=x, DstT=DstT, Truncate=Truncate, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 3274, in create_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1770, in __init__
self._traceback = tf_stack.extract_stack()
UnimplementedError (see above for traceback): Cast string to float is not supported
[[node linear/linear/ToFloat (defined at /usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/feature_column.py:1875) = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, Truncate=false, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Const_1)]]
这是我使用的代码
#!/usr/bin/env python
# -*- coding: utf-8-*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas as pd
import tensorflow as tf
import tempfile
import numpy as np
import shutil
tf.logging.set_verbosity(tf.logging.INFO)
COLUMNS = ["TS", "DT", "SO", "TY", "JX", "CN", "SN", "RE", "TN", "PE", "GR", "PR", "KG", "ME", "MB", "MM", "NM", "TH", "MSN", "LE", "OF", "BL", "CK", "CR", "LCR"]``
train_file = "/home/davide/Scrivania/NH3-TRAIN.csv"
test_file = "/home/davide/Scrivania/NH3-TEST.csv"
df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True)
df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True)
LABEL10_COLUMN = "label10"
df_train[LABEL10_COLUMN] = (df_train["CK"].apply(lambda x: "SI" in x)).astype(int)
df_test[LABEL10_COLUMN] = (df_test["CK"].apply(lambda x: "SI" in x)).astype(int)
CATEGORICAL_COLUMNS = ["SO", "TY", "JX", "CN", "SN", "RE", "TN", "MSN", "LE", "OF", "BL", "CK", "CR", "LCR"]
CONTINUOUS_COLUMNS = ["TS", "DT", "PE", "GR", "PR", "KG", "ME", "MB", "MM", "NM", "TH"]
def input_fn(df):
continuous_cols = {k: tf.constant(df[k].values)
for k in CONTINUOUS_COLUMNS}
categorical_cols = {k: tf.SparseTensor(
indices=[[i, 0] for i in range(df[k].size)],
values=df[k].astype(str).values,
dense_shape=[df[k].size, 1])
for k in CATEGORICAL_COLUMNS}
feature_cols = dict(continuous_cols.items() + categorical_cols.items())
label10 = tf.constant(df[LABEL10_COLUMN].values)
return feature_cols, label10
def train_input_fn():
return input_fn(df_train)
def eval_input_fn():
return input_fn(df_test)
TS = tf.contrib.layers.real_valued_column("TS")
DT = tf.contrib.layers.real_valued_column("DT")
SO = tf.contrib.layers.sparse_column_with_hash_bucket("SO", hash_bucket_size=200, dtype=tf.string)
TY = tf.contrib.layers.sparse_column_with_keys(column_name='TY', keys=["ALM","NR","ACK","LLL","HHH","HTRP","IOP","LTRP"],default_value=-1, combiner='sum', dtype=tf.string)
JX = tf.contrib.layers.sparse_column_with_hash_bucket("JX", hash_bucket_size=70)
CN = tf.contrib.layers.sparse_column_with_keys(column_name="CN", keys=["ALM","NR","ACK","LLL","HHH","HTRP","IOP","LTRP"],default_value=-1, combiner='sum', dtype=tf.string)
SN = tf.contrib.layers.sparse_column_with_keys(column_name="SN", keys=["ALM","NR","ACK","LLL","HHH","HTRP","IOP","LTRP"],default_value=-1, combiner='sum', dtype=tf.string)
RE = tf.contrib.layers.sparse_column_with_keys(column_name="RE", keys=["ZERO","UNO"],default_value=-1, combiner='sum', dtype=tf.string)
TN = tf.contrib.layers.sparse_column_with_hash_bucket("TN", hash_bucket_size=200)
PE = tf.contrib.layers.real_valued_column("PE")
GR = tf.contrib.layers.real_valued_column("GR")
PR = tf.contrib.layers.real_valued_column("PR")
KG = tf.contrib.layers.real_valued_column("KG")
ME = tf.contrib.layers.real_valued_column("ME")
MB = tf.contrib.layers.real_valued_column("MB")
MM = tf.contrib.layers.real_valued_column("MM")
NM = tf.contrib.layers.real_valued_column("NM")
TH = tf.contrib.layers.real_valued_column("TH")
MSN = tf.contrib.layers.sparse_column_with_keys(column_name="MSN", keys=["ALM","NR","ACK","LLL","HHH","Na","HTRP","IOP","LTRP"],default_value=-1, combiner='sum', dtype=tf.string)
LE = tf.contrib.layers.sparse_column_with_keys(column_name="LE", keys=["ZERO","DUE"],default_value=-1, combiner='sum', dtype=tf.string)
OF = tf.contrib.layers.sparse_column_with_keys(column_name="OF", keys=["ZERO","UNO"],default_value=-1, combiner='sum', dtype=tf.string)
BL = tf.contrib.layers.sparse_column_with_keys(column_name="BL", keys=["ZERO","UNO"],default_value=-1, combiner='sum', dtype=tf.string)
CK = tf.contrib.layers.sparse_column_with_keys(column_name="CK", keys=["SI","NO", "Na"],default_value=-1, combiner='sum', dtype=tf.string)
CR = tf.contrib.layers.sparse_column_with_keys(column_name="CR", keys=["SI","NO","Na"],default_value=-1, combiner='sum', dtype=tf.string)
LCR = tf.contrib.layers.sparse_column_with_keys(column_name="LCR", keys=["SI","NO","Na"],default_value=-1, combiner='sum', dtype=tf.string)
COND = tf.contrib.layers.crossed_column([SO, TN], hash_bucket_size=int(1e5))
TYPE = tf.contrib.layers.crossed_column([TY, JX, CN, SN, MSN], hash_bucket_size=int(1e6))
ALARM = tf.contrib.layers.crossed_column([LE, OF, BL], hash_bucket_size=10)
model_dir = tempfile.mkdtemp()
m = tf.contrib.learn.LinearClassifier(
feature_columns=[TS, DT, SO, TY, JX, CN, SN, RE, TN, PE, GR, PR, KG, ME, MB, MM, NM, TH, MSN, LE, OF, BL, CR, LCR, COND, TYPE, ALARM],
optimizer=tf.train.FtrlOptimizer(
learning_rate=0.1,
l1_regularization_strength=0.001),
model_dir=model_dir)
m.fit(input_fn=train_input_fn, steps=2000)
results = m.evaluate(input_fn=eval_input_fn, steps=1)
for key in sorted(results):
print("%s: %s" % (key, results[key]))
predictions=list(m.predict_proba(input_fn=eval_input_fn))
print("\n".join(map(str, predictions)))
我已经看到过类似tf.strings.to_number之类的东西，可以将字符串转换为浮点数，但是没有看到可以帮助我编辑此代码的东西。我没有安装允许我使用GPU的东西。 谢谢