问题
I'm trying to build a simple linear model using TensorFlow functional API.
def create_model():
input1 = tf.keras.Input(shape=(30,))
hidden1 = tf.keras.layers.Dense(units = 12, activation='relu')(input1)
hidden2 = tf.keras.layers.Dense(units = 6, activation='relu')(hidden1)
output1 = tf.keras.layers.Dense(units = 2, activation='softmax')(hidden2)
model = tf.keras.models.Model(inputs = input1, outputs = output1)
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
This is my code to create the model.
I'm using a data pipeline to create the input dataset like this.
def make_dataset(dataframe, shuffle=True, batch_size=32):
labels = dataframe.pop('target')
ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
if shuffle:
ds = ds.shuffle(buffer_size=100000, seed = 121 ).repeat()
return ds
pos_ds = make_dataset(train_data_pos)
neg_ds = make_dataset(train_data_neg)
train_ds = tf.data.experimental.sample_from_datasets([pos_ds, neg_ds], weights=[0.5, 0.5], seed = 45)
train_ds = train_ds.batch(BATCH_SIZE)
steps_per_epoch = np.ceil(2.0*count_neg/BATCH_SIZE)
Here the train_data_pos and train_data_neg are data frame containing positive and negative classes
history = model.fit(train_ds,
validation_data=val_ds,
epochs=100,
verbose = 1,
steps_per_epoch=steps_per_epoch)
This is my model.fit() cmd.
My error log is as follows:
Traceback (most recent call last):
File "6.py", line 159, in <module>
steps_per_epoch=steps_per_epoch)
File "C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\keras\engine\training.py", line 66, in _method_wrapper
return method(self, *args, **kwargs)
File "C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\keras\engine\training.py", line 848, in fit
tmp_logs = train_function(iterator)
File "C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\eager\def_function.py", line 580, in __call__
result = self._call(*args, **kwds)
File "C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\eager\def_function.py", line 627, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\eager\def_function.py", line 506, in _initialize
*args, **kwds))
File "C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\eager\function.py", line 2446, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\eager\function.py", line 2777, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\eager\function.py", line 2667, in _create_graph_function
capture_by_value=self._capture_by_value),
File "C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\framework\func_graph.py", line 981, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\eager\def_function.py", line 441, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\framework\func_graph.py", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
tensorflow.python.autograph.pyct.error_utils.KeyError: in user code:
C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\keras\engine\training.py:571 train_function *
outputs = self.distribute_strategy.run(
C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\keras\engine\training.py:531 train_step **
y_pred = self(x, training=True)
C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:927 __call__
outputs = call_fn(cast_inputs, *args, **kwargs)
C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\keras\engine\network.py:719 call
convert_kwargs_to_constants=base_layer_utils.call_context().saving)
C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\keras\engine\network.py:826 _run_internal_graph
inputs = self._flatten_to_reference_inputs(inputs)
C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\keras\engine\network.py:926 _flatten_to_reference_inputs
return [tensors[inp._keras_history.layer.name] for inp in ref_inputs]
C:\Users\Aniket\Documents\Aniket\learning-ML\ML_env\lib\site-packages\tensorflow\python\keras\engine\network.py:926 <listcomp>
return [tensors[inp._keras_history.layer.name] for inp in ref_inputs]
KeyError: 'input_1'
All of this works when I use sequential API to construct the model.
def create_model():
model = tf.keras.Sequential([
feature_layer,
tf.keras.layers.Dense(units = 12, activation='relu', use_bias = True, kernel_initializer= 'glorot_uniform', bias_initializer = 'glorot_uniform', name = 'd1'),
tf.keras.layers.Dense(units = 6, activation='relu', use_bias = True, kernel_initializer= 'glorot_uniform', bias_initializer = 'glorot_uniform', name = 'd2'),
tf.keras.layers.Dense(units = 2, activation='softmax', name = 'out')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
Here feature_layer is tf.keras.layers.DenseFeatures
Here is the link to the entire code - LINK
回答1:
Since you have feature columns that are not directly compatible with tf.keras.Input, you have to use a workaround that assigns tf.keras.Input to each original feature column. The workaround via this Github issue is:
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import feature_column
from tensorflow import keras
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
URL = 'https://storage.googleapis.com/applied-dl/heart.csv'
dataframe = pd.read_csv(URL)
dataframe.head()
train, test = train_test_split(dataframe, test_size=0.2)
train, val = train_test_split(train, test_size=0.2)
print(len(train), 'train examples')
print(len(val), 'validation examples')
print(len(test), 'test examples')
# A utility method to create a tf.data dataset from a Pandas Dataframe
def df_to_dataset(dataframe, shuffle=True, batch_size=32):
dataframe = dataframe.copy()
labels = dataframe.pop('target')
ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
if shuffle:
ds = ds.shuffle(buffer_size=len(dataframe))
ds = ds.batch(batch_size)
return ds
batch_size = 5 # A small batch sized is used for demonstration purposes
train_ds = df_to_dataset(train, batch_size=batch_size)
val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)
test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)
def create_inputs():
age = feature_column.numeric_column("age")
feature_columns = []
feature_layer_inputs = {}
# numeric cols
for header in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'slope', 'ca']:
feature_columns.append(feature_column.numeric_column(header))
feature_layer_inputs[header] = tf.keras.Input(shape=(1,), name=header)
# bucketized cols
age_buckets = feature_column.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
feature_columns.append(age_buckets)
# indicator cols
thal = feature_column.categorical_column_with_vocabulary_list(
'thal', ['fixed', 'normal', 'reversible'])
thal_one_hot = feature_column.indicator_column(thal)
feature_columns.append(thal_one_hot)
feature_layer_inputs['thal'] = tf.keras.Input(shape=(1,), name='thal', dtype=tf.string)
# embedding cols
thal_embedding = feature_column.embedding_column(thal, dimension=8)
feature_columns.append(thal_embedding)
# crossed cols
crossed_feature = feature_column.crossed_column([age_buckets, thal], hash_bucket_size=1000)
crossed_feature = feature_column.indicator_column(crossed_feature)
feature_columns.append(crossed_feature)
return feature_columns, feature_layer_inputs
def create_model():
feature_columns, feature_layer_inputs = create_inputs()
dense_features = tf.keras.layers.DenseFeatures(feature_columns)(feature_layer_inputs)
hidden1 = tf.keras.layers.Dense(units = 12, activation='relu')(dense_features)
hidden2 = tf.keras.layers.Dense(units = 6, activation='relu')(hidden1)
output1 = tf.keras.layers.Dense(units = 2, activation='softmax')(hidden2)
model = tf.keras.models.Model(
inputs = [v for v in feature_layer_inputs.values()],
outputs = output1)
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy', metrics=['accuracy'])
return model
model = create_model()
model.fit(train_ds)
来源：https://stackoverflow.com/questions/61994299/using-functional-api-in-tensorflow-throws-keyerror-input-1