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私はテンソルフローとこの分野の初心者ですが、私はCS231nクラスのAndrej Karpathyの講義をすべて見てきましたので、私はそのコードを理解しています。テンソルフローで予測する
だから、これはコード（ない鉱山）である：（私は私が作成しようとしています最後に書いた部分のために受け入れることがRNNのスパム分類器だし、それは素晴らしい働いてhttps://github.com/nfmcclure/tensorflow_cookbook/tree/master/09_Recurrent_Neural_Networks/02_Implementing_RNN_for_Spam_Prediction
# Implementing an RNN in TensorFlow
# ----------------------------------
#
# We implement an RNN in TensorFlow to predict spam/ham from texts
#
# https://github.com/nfmcclure/tensorflow_cookbook/blob/master/09_Recurrent_Neural_Networks/02_Implementing_RNN_for_Spam_Prediction/02_implementing_rnn.py
import os
import re
import io
import glob
import requests
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from zipfile import ZipFile
from tensorflow.python.framework import ops
ops.reset_default_graph()
# Start a graph
sess = tf.Session()
# Set RNN parameters
epochs = 20
batch_size = 250
max_sequence_length = 25
rnn_size = 10
embedding_size = 50
min_word_frequency = 10
learning_rate = 0.0005
dropout_keep_prob = tf.placeholder(tf.float32)
# Download or open data
data_dir = 'temp'
data_file = 'text_data.txt'
if not os.path.exists(data_dir):
os.makedirs(data_dir)
if not os.path.isfile(os.path.join(data_dir, data_file)):
zip_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip'
r = requests.get(zip_url)
z = ZipFile(io.BytesIO(r.content))
file = z.read('SMSSpamCollection')
# Format Data
text_data = file.decode()
text_data = text_data.encode('ascii', errors='ignore')
text_data = text_data.decode().split('\n')
# Save data to text file
with open(os.path.join(data_dir, data_file), 'w') as file_conn:
for text in text_data:
file_conn.write("{}\n".format(text))
else:
# Open data from text file
text_data = []
with open(os.path.join(data_dir, data_file), 'r') as file_conn:
for row in file_conn:
text_data.append(row)
text_data = text_data[:-1]
text_data = [x.split('\t') for x in text_data if len(x) >= 1]
text_data = [x for x in text_data if len(x) > 1]
print([list(x) for x in zip(*text_data)])
[text_data_target, text_data_train] = [list(x) for x in zip(*text_data)]
# Create a text cleaning function
def clean_text(text_string):
text_string = re.sub(r'([^\s\w]|_|[0-9])+', '', text_string)
text_string = " ".join(text_string.split())
text_string = text_string.lower()
return (text_string)
# Clean texts
text_data_train = [clean_text(x) for x in text_data_train]
# Change texts into numeric vectors
vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(max_sequence_length,
min_frequency=min_word_frequency)
text_processed = np.array(list(vocab_processor.fit_transform(text_data_train)))
# Shuffle and split data
text_processed = np.array(text_processed)
text_data_target = np.array([1 if x == 'ham' else 0 for x in text_data_target])
shuffled_ix = np.random.permutation(np.arange(len(text_data_target)))
x_shuffled = text_processed[shuffled_ix]
y_shuffled = text_data_target[shuffled_ix]
# Split train/test set
ix_cutoff = int(len(y_shuffled) * 0.80)
x_train, x_test = x_shuffled[:ix_cutoff], x_shuffled[ix_cutoff:]
y_train, y_test = y_shuffled[:ix_cutoff], y_shuffled[ix_cutoff:]
vocab_size = len(vocab_processor.vocabulary_)
print("Vocabulary Size: {:d}".format(vocab_size))
print("80-20 Train Test split: {:d} -- {:d}".format(len(y_train), len(y_test)))
# Create placeholders
x_data = tf.placeholder(tf.int32, [None, max_sequence_length])
y_output = tf.placeholder(tf.int32, [None])
# Create embedding
embedding_mat = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0))
embedding_output = tf.nn.embedding_lookup(embedding_mat, x_data)
# embedding_output_expanded = tf.expand_dims(embedding_output, -1)
# Define the RNN cell
# tensorflow change >= 1.0, rnn is put into tensorflow.contrib directory. Prior version not test.
if tf.__version__[0] >= '1':
cell = tf.contrib.rnn.BasicRNNCell(num_units=rnn_size)
else:
cell = tf.nn.rnn_cell.BasicRNNCell(num_units=rnn_size)
output, state = tf.nn.dynamic_rnn(cell, embedding_output, dtype=tf.float32)
output = tf.nn.dropout(output, dropout_keep_prob)
# Get output of RNN sequence
output = tf.transpose(output, [1, 0, 2])
last = tf.gather(output, int(output.get_shape()[0]) - 1)
weight = tf.Variable(tf.truncated_normal([rnn_size, 2], stddev=0.1))
bias = tf.Variable(tf.constant(0.1, shape=[2]))
logits_out = tf.matmul(last, weight) + bias
# Loss function
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_out,
labels=y_output) # logits=float32, labels=int32
loss = tf.reduce_mean(losses)
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits_out, 1), tf.cast(y_output, tf.int64)), tf.float32))
optimizer = tf.train.RMSPropOptimizer(learning_rate)
train_step = optimizer.minimize(loss)
init = tf.global_variables_initializer()
sess.run(init)
train_loss = []
test_loss = []
train_accuracy = []
test_accuracy = []
# Start training
for epoch in range(epochs):
# Shuffle training data
shuffled_ix = np.random.permutation(np.arange(len(x_train)))
x_train = x_train[shuffled_ix]
y_train = y_train[shuffled_ix]
num_batches = int(len(x_train)/batch_size) + 1
# TO DO CALCULATE GENERATIONS ExACTLY
for i in range(num_batches):
# Select train data
min_ix = i * batch_size
max_ix = np.min([len(x_train), ((i + 1) * batch_size)])
x_train_batch = x_train[min_ix:max_ix]
y_train_batch = y_train[min_ix:max_ix]
# Run train step
train_dict = {x_data: x_train_batch, y_output: y_train_batch, dropout_keep_prob: 0.5}
sess.run(train_step, feed_dict=train_dict)
# Run loss and accuracy for training
temp_train_loss, temp_train_acc = sess.run([loss, accuracy], feed_dict=train_dict)
train_loss.append(temp_train_loss)
train_accuracy.append(temp_train_acc)
# Run Eval Step
test_dict = {x_data: x_test, y_output: y_test, dropout_keep_prob: 1.0}
temp_test_loss, temp_test_acc = sess.run([loss, accuracy], feed_dict=test_dict)
test_loss.append(temp_test_loss)
test_accuracy.append(temp_test_acc)
print('Epoch: {}, Test Loss: {:.2}, Test Acc: {:.2}'.format(epoch + 1, temp_test_loss, temp_test_acc))
# Plot loss over time
epoch_seq = np.arange(1, epochs + 1)
plt.plot(epoch_seq, train_loss, 'k--', label='Train Set')
plt.plot(epoch_seq, test_loss, 'r-', label='Test Set')
plt.title('Softmax Loss')
plt.xlabel('Epochs')
plt.ylabel('Softmax Loss')
plt.legend(loc='upper left')
plt.show()
# Plot accuracy over time
plt.plot(epoch_seq, train_accuracy, 'k--', label='Train Set')
plt.plot(epoch_seq, test_accuracy, 'r-', label='Test Set')
plt.title('Test Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend(loc='upper left')
plt.show()
def findFiles(path): return glob.glob(path)
pred_array = "words"
pred_num = np.array(list(vocab_processor.fit_transform(pred_array)))
print(pred_num)
pred_output = tf.placeholder(tf.float32,[1,len(pred_array),max_sequence_length])
feed_dict = {pred_output: [pred_num]}
classification = sess.run(losses, feed_dict)
print(classification)
予測）。私はちょうどこれに予測関数を作成する方法を理解したいんだ
、そのようなもの：
def predict(text): # text is a string (my mail)
# Doing prediction stuff
return (top result) # ham or spam
最後の数行は、私の最後の試みは私に次のエラーを与えているされています tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=<unknown>, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]私はちょうど初心者が正しくありませんよので...
を
はまた、私はMaking predictions with a TensorFlow modelを使って何かをしようとした、と私はまたhttps://www.tensorflow.org/serving/serving_basicを読んで、私が試したすべてのものが失敗しましたanationsは歓迎されていますが、私はそれをコードする方法を知っているかどうか分からないので、コードの回答も投稿してください。
（Python 3.6 btw）
ありがとうございました！
それがどのように失敗しました：
だからあなたのような何かを試してみてください？最小限の例を提供できますか？ – Harald
さて、私は何をしているのか分かりません...しかし、今はInvalidArgumentError（上記のトレースバック参照）：dtype floatを使ってプレースホルダテンソル 'Placeholder'の値を入力する必要があります \t [[Node： =プレースホルダ[dtype = DT_FLOAT、shape =、_device = "/ job：localhost/replica：0/task：0/cpu：0"]（）]] @Harald –
yon
私はいくつかのものを修正しましたが、同じエラーは、何が起こっているのかまだ分かりません。 @ハラルド助けてください？ – yon