tf/python的新功能，并创建了一个模型，将文本分类为毒性级别(淫秽，有毒，威胁等)。这是我到目前为止所拥有的，它确实产生摘要，所以我知道它正在正确加载。我如何将文本传递给模型以返回预测?如有任何帮助，不胜感激。
import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
checkpoint_path = "tf_model/the_model/saved_model.pb"
checkpoint_dir = os.path.dirname(checkpoint_path)
new_model = tf.keras.models.load_model(checkpoint_dir)
# Check its architecture
new_model.summary()
inputs = [
"tenserflow seems like it fits the bill but there are zero tutorials that outline how to reuse a model in a production environment "
]
predictions = new_model.predict(inputs)
print(predictions)
我得到许多错误消息，一些冗长的消息如下:
警告:tensorflow:模型被构造与形状(None, 150)的输入KerasTensor(type_spec=TensorSpec(shape=(None, 150)， dtype=tf。float32, name='input_1')， name='input_1'， description="创建的层'input_1'")，但它被调用的输入与不兼容的形状(None, 1)。
ValueError:"{{node model/conv1d/conv1d}} = Conv2D[T=DT_FLOAT, data_format="NHWC"， dilations=[1,1,1,1]， explicit_paddings=[]， padding="VALID"， strides=[1,1,1,1]， use_cudnn_on_gpu=true](model/conv1d/conv1d/ExpandDims_1, model/conv1d/conv1d/ExpandDims_1)"，输入形状:[?, 1256),(3256, 64)。
这是用于创建和测试它/预测的py代码，它完美地工作:
import tensorflow as tf
import numpy as np
import pandas as pd
import os
TRAIN_DATA = "datasets/train.csv"
GLOVE_EMBEDDING = "embedding/glove.6B.100d.txt"
train = pd.read_csv(TRAIN_DATA)
train["comment_text"].fillna("fillna")
x_train = train["comment_text"].str.lower()
y_train = train[["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]].values
max_words = 100000
max_len = 150
embed_size = 100
tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=max_words, lower=True)
tokenizer.fit_on_texts(x_train)
x_train = tokenizer.texts_to_sequences(x_train)
x_train = tf.keras.preprocessing.sequence.pad_sequences(x_train, maxlen=max_len)
embeddings_index = {}
with open(GLOVE_EMBEDDING, encoding='utf8') as f:
for line in f:
values = line.rstrip().rsplit(' ')
word = values[0]
embed = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = embed
word_index = tokenizer.word_index
num_words = min(max_words, len(word_index) + 1)
embedding_matrix = np.zeros((num_words, embed_size), dtype='float32')
for word, i in word_index.items():
if i >= max_words:
continue
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
input = tf.keras.layers.Input(shape=(max_len,))
x = tf.keras.layers.Embedding(max_words, embed_size, weights=[embedding_matrix], trainable=False)(input)
x = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(128, return_sequences=True, dropout=0.1,
recurrent_dropout=0.1))(x)
x = tf.keras.layers.Conv1D(64, kernel_size=3, padding="valid", kernel_initializer="glorot_uniform")(x)
avg_pool = tf.keras.layers.GlobalAveragePooling1D()(x)
max_pool = tf.keras.layers.GlobalMaxPooling1D()(x)
x = tf.keras.layers.concatenate([avg_pool, max_pool])
preds = tf.keras.layers.Dense(6, activation="sigmoid")(x)
model = tf.keras.Model(input, preds)
model.summary()
model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(lr=1e-3), metrics=['accuracy'])
batch_size = 128
checkpoint_path = "tf_model/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,
save_weights_only=True,
verbose=1)
callbacks = [
tf.keras.callbacks.EarlyStopping(patience=5, monitor='val_loss'),
tf.keras.callbacks.TensorBoard(log_dir='logs'),
cp_callback
]
model.fit(x_train, y_train, validation_split=0.2, batch_size=batch_size,
epochs=1, callbacks=callbacks, verbose=1)
latest = tf.train.latest_checkpoint(checkpoint_dir)
model.load_weights(latest)
# Save the entire model as a SavedModel.
model.save('tf_model/the_model')
predictions = model.predict(np.expand_dims(x_train[42], 0))
print(tokenizer.sequences_to_texts([x_train[42]]))
print(y_train[42])
print(predictions)
最终解决方案:
import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
checkpoint_path = "tf_model/the_model/saved_model.pb"
checkpoint_dir = os.path.dirname(checkpoint_path)
new_model = tf.keras.models.load_model(checkpoint_dir)
max_words = 100000
max_len = 150
# Check its architecture
# new_model.summary()
inputs = ["tenserflow seems like it fits the bill but there are zero tutorials that outline how to reuse a model in a production environment."]
# use same tokenizer used to build model
tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=max_words, lower=True)
tokenizer.fit_on_texts(inputs)
# pass string to tokenizer and that 'array' is passed to predict
sequence = tokenizer.texts_to_sequences(inputs) # same tokenizer which is used on train data.
sequence = tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen = max_len)
predictions = new_model.predict(sequence)
print(predictions)
# [[0.0365479 0.01275077 0.02102855 0.00647011 0.02302513 0.00406089]]
需要以同样的方式处理。可以使用以下命令:
inputs = [
"tenserflow seems like it fits the bill but there are zero tutorials that outline
how to reuse a model in a production environment"]
sequence = tokenizer.texts_to_sequences(inputs) # same tokenizer which is used on train data.
sequence = pad_sequences(sequence, maxlen = max_len)
predictions = new_model.predict(sequence)