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Problems with tensorflow

So I am getting an error
 ValueError: Feature (key: age) cannot have rank 0. Given: Tensor("linealinear_model/Cast:0", shape=(), dtype=float32) 
and my code is :
#!/usein/env python from __future__ import absolute_import, division, print_function, unicode_literals import numpy as np import pandas as pd import matplotlib.pyplot as plt from IPython.display import clear_output from six.moves import urllib import tensorflow.compat.v2.feature_column as fc import tensorflow as tf dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv') # training data dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv') # testing data y_train = dftrain.pop('survived') y_eval = dfeval.pop('survived') #print(dftrain.loc[0], y_train.loc[0]) #pd.concat([dftrain, y_train], axis=1).groupby('sex').survived.mean().plot(kind='barh').set_xlabel('% survive') #plt.show() CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck', 'embark_town', 'alone'] NUMERIC_COLUMNS = ['age', 'fare'] feature_columns = [] for feature_name in CATEGORICAL_COLUMNS: vocabulary = dftrain[feature_name].unique() feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary)) for feature_name in NUMERIC_COLUMNS: feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32)) def make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32): def input_function(): ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df)) if shuffle: ds = ds.shuffle(1000) ds = ds.batch(batch_size).repeat(num_epochs) return ds return input_function train_input_fn = make_input_fn(dftrain, y_train) eval_input_fn = make_input_fn(dfeval, y_eval, num_epochs=1, shuffle=False) ds = make_input_fn(dftrain, y_train, batch_size=10)() for feature_batch, label_batch in ds.take(1): print('Some feature keys:', list(feature_batch.keys())) print() print('A batch of class:', feature_batch['class'].numpy()) print() print('A batch of Labels:', label_batch.numpy()) #gender_column = feature_columns[0] #tf.keras.layers.DenseFeatures([tf.feature_column.indicator_column(gender_column)])(feature_batch).numpy() linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns) linear_est.train(train_input_fn) result = linear_est.evaluate(eval_input_fn) clear_output() print(result) 
which is just a copy of code from tensorflow website:
https://www.tensorflow.org/tutorials/estimatolinear
up to derrived feature columns
I can see gpu usage while program is running, so it is training, the only thing it doesn't work is .evaluate function. I am using cuda 10.1 and all newest modules installed with pip in anaconda virtual environment,if it is important

I would appreciate if someone could point out my mistake
Full output:
2020-11-17 12:07:53.252992: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll WARNING:tensorflow:Using temporary folder as model directory: C:\Users\jokub\AppData\Local\Temp\tmpw_wswl1r WARNING:tensorflow:From C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\training_util.py:235: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. 2020-11-17 12:07:55.431889: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library nvcuda.dll 2020-11-17 12:07:55.462799: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: pciBusID: 0000:01:00.0 name: GeForce GTX 1660 Ti computeCapability: 7.5 coreClock: 1.59GHz coreCount: 24 deviceMemorySize: 6.00GiB deviceMemoryBandwidth: 268.26GiB/s 2020-11-17 12:07:55.471235: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll 2020-11-17 12:07:55.477449: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll 2020-11-17 12:07:55.484076: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll 2020-11-17 12:07:55.489859: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll 2020-11-17 12:07:55.496662: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll 2020-11-17 12:07:55.503580: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll 2020-11-17 12:07:55.513128: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll 2020-11-17 12:07:55.518329: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0 WARNING:tensorflow:Layer linealinear_model is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because its dtype defaults to floatx. If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2. To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor. WARNING:tensorflow:From C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\linear.py:1471: Layer.add_variable (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version. Instructions for updating: Please use `layer.add_weight` method instead. WARNING:tensorflow:From C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\optimizer_v2\ftrl.py:111: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor 2020-11-17 12:07:58.016959: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2020-11-17 12:07:58.031298: I tensorflow/compilexla/service/service.cc:168] XLA service 0x2c7fc587d00 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2020-11-17 12:07:58.037092: I tensorflow/compilexla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2020-11-17 12:07:58.039632: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: pciBusID: 0000:01:00.0 name: GeForce GTX 1660 Ti computeCapability: 7.5 coreClock: 1.59GHz coreCount: 24 deviceMemorySize: 6.00GiB deviceMemoryBandwidth: 268.26GiB/s 2020-11-17 12:07:58.046271: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll 2020-11-17 12:07:58.049891: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll 2020-11-17 12:07:58.053473: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cufft64_10.dll 2020-11-17 12:07:58.057026: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library curand64_10.dll 2020-11-17 12:07:58.061044: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cusolver64_10.dll 2020-11-17 12:07:58.065359: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cusparse64_10.dll 2020-11-17 12:07:58.068513: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cudnn64_7.dll 2020-11-17 12:07:58.073388: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0 2020-11-17 12:07:58.609527: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix: 2020-11-17 12:07:58.614946: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263] 0 2020-11-17 12:07:58.616823: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0: N 2020-11-17 12:07:58.619022: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4615 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5) 2020-11-17 12:07:58.628434: I tensorflow/compilexla/service/service.cc:168] XLA service 0x2c79fdecad0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2020-11-17 12:07:58.632591: I tensorflow/compilexla/service/service.cc:176] StreamExecutor device (0): GeForce GTX 1660 Ti, Compute Capability 7.5 2020-11-17 12:08:02.446830: I tensorflow/stream_executoplatform/default/dso_loader.cc:48] Successfully opened dynamic library cublas64_10.dll WARNING:tensorflow:Layer linealinear_model is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because its dtype defaults to floatx. If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2. To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor. Traceback (most recent call last): File "AI_thingy.py", line 69, in  result = linear_est.evaluate(eval_input_fn) File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 461, in evaluate return self._actual_eval( File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 510, in _actual_eval return _evaluate() File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 492, in _evaluate self._evaluate_build_graph(input_fn, hooks, checkpoint_path)) File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1528, in _evaluate_build_graph self._call_model_fn_eval(input_fn, self.config)) File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1563, in _call_model_fn_eval estimator_spec = self._call_model_fn(features, labels, ModeKeys.EVAL, File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1163, in _call_model_fn model_fn_results = self._model_fn(features=features, **kwargs) File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\linear.py", line 937, in _model_fn return _linear_model_fn_v2( File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\linear.py", line 665, in _linear_model_fn_v2 logits, trainable_variables = _linear_model_fn_builder_v2( File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\linear.py", line 602, in _linear_model_fn_builder_v2 logits = linear_model(features) File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer_v1.py", line 776, in __call__ outputs = call_fn(cast_inputs, *args, **kwargs) File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\autograph\impl\api.py", line 258, in wrapper raise e.ag_error_metadata.to_exception(e) ValueError: in user code: C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\linear.py:1671 call * return self.layer(features) C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\linear.py:1499 call * weighted_sum = fc_v2._create_weighted_sum( # pylint: disable=protected-access C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py:2140 _create_weighted_sum ** return _create_dense_column_weighted_sum( C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py:2150 _create_dense_column_weighted_sum tensor = column.get_dense_tensor(transformation_cache, state_manager) C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py:2592 get_dense_tensor return transformation_cache.get(self, state_manager) C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py:2355 get transformed = column.transform_feature(self, state_manager) C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py:2564 transform_feature input_tensor = transformation_cache.get(self.key, state_manager) C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py:2334 get feature_tensor = self._get_raw_feature_as_tensor(key) C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\feature_column\feature_column_v2.py:2394 _get_raw_feature_as_tensor raise ValueError( ValueError: Feature (key: age) cannot have rank 0. Given: Tensor("linealinear_model/Cast:0", shape=(), dtype=float32) 
submitted by jkokas to learnpython

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