Welcome to OGeek Q&A Community for programmer and developer-Open, Learning and Share
Welcome To Ask or Share your Answers For Others

Categories

0 votes
1.3k views
in Technique[技术] by (71.8m points)

python - Tensorflow estimator ValueError: logits and labels must have the same shape ((?, 1) vs (?,))

I'm relatively new to ML, thought I'll start with keras. Here I'm classifying movie reviews as positive or negative using binary crossentropy. So, when I'm trying to wrap my keras model with tensorflow estimator, I get the error:

Tensorflow estimator ValueError: logits and labels must have the same shape ((?, 1) vs (?,))

I'm using sigmoid activation as my last layer, guess I'm missing something trivial here. Any help?

from tensorflow import keras
import tensorflow as tf
print("Tensorflow {} loaded".format(tf.__version__))
import numpy as np

keras.__version__
from keras.datasets import imdb

(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
def vectorize_sequences(sequences, dimension=10000):
    # Create an all-zero matrix of shape (len(sequences), dimension)
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.  # set specific indices of results[i] to 1s
    return results.astype('float32')

# Our vectorized training data
x_train = vectorize_sequences(train_data)

# Our vectorized test data
x_test = vectorize_sequences(test_data)

# Our vectorized labels
y_train = np.asarray(train_labels).astype('float32')
y_test = np.asarray(test_labels).astype('float32')

x_val = x_train[:10000]
partial_x_train = x_train[10000:]
y_val = y_train[:10000]
partial_y_train = y_train[10000:]

model = keras.models.Sequential()
model.add(keras.layers.Dense(16, activation='relu', input_shape=(10000,), name='reviews'))
model.add(keras.layers.Dense(16, activation='relu'))
model.add(keras.layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])
estimator_model = keras.estimator.model_to_estimator(keras_model=model)

def input_function(features,labels=None,shuffle=False,epochs=None,batch_size=None):
    input_fn = tf.estimator.inputs.numpy_input_fn(
        x={"reviews_input": features},
        y=labels,
        shuffle=shuffle,
        num_epochs=epochs,
        batch_size=batch_size
    )
    return input_fn

estimator_model.train(input_fn=input_function(partial_x_train, partial_y_train, True,20,512))
score = estimator_model.evaluate(input_function(x_val, labels=y_val))
print(score)
See Question&Answers more detail:os

与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…
Welcome To Ask or Share your Answers For Others

1 Reply

0 votes
by (71.8m points)

You should reshape your labels as 2d-tensor (the first dimension will be the batch dimension and the second the scalar label):

# Our vectorized labels
y_train = np.asarray(train_labels).astype('float32').reshape((-1,1))
y_test = np.asarray(test_labels).astype('float32').reshape((-1,1))

与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…
OGeek|极客中国-欢迎来到极客的世界,一个免费开放的程序员编程交流平台!开放,进步,分享!让技术改变生活,让极客改变未来! Welcome to OGeek Q&A Community for programmer and developer-Open, Learning and Share
Click Here to Ask a Question

1.4m articles

1.4m replys

5 comments

57.0k users

...