问题
I'm really a beginner with tensor flow and in all of this field, but I've seen all the lectures of Andrej Karpathy in CS231n class so I'm understanding the code.
So this is the code (not mine): 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)
It's a RNN spam classifier, and It's working great (accept for the part I wrote at the end where I'm trying to create the predictions).
I'm just want to understand how to create a prediction function to this, something that looks like that:
def predict(text): # text is a string (my mail)
# Doing prediction stuff
return (top result) # ham or spam
The last few lines are my last try is giving me the following error:
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"]()]]
Also I tried to do something using Making predictions with a TensorFlow model, and I also read https://www.tensorflow.org/serving/serving_basic and every thing I've tried failed...
Since I'm just a beginner explanations are welcomed, but I'm not sure I'll know how to code it so can you please post the code answer too.
(Python 3.6 btw)
Thanks!
回答1:
If you take a look at how the original code does the training and testing steps, specifically how they set up their train_dict and test_dict, you see that they feed values to each of the tensors defined as placeholder in the graph. Basically placeholders need to be given some value if they are going to be used in whatever calculation you are asking your network to do. Since you are looking for predictions from the network, you probably do not need to provide an expected output, but you will need to give it input data x_data, and a value for dropout_keep_prob. This should be dropout_keep_prob=1.0 for prediction.
You also want a prediction, not the loss of the network. The loss is basically a measure of how far your network's output is from what you expect, but since you are trying to predict something for new data you really just want to see what the network says it is. You can do this using the logits_out op directly, or we can add an op that converts your logits into a probability distribution over your classes. Either way you can look at the distribution to get an idea of how likely the network thinks your data falls into each category, or you can take the max value of this vector to just output the network's best guess.
So you might try something like:
prediction = tf.nn.softmax(logits_out)
feed_dict = {x_data: your_input_data, dropout_keep_prob: 1.0}
pred = sess.run(prediction, feed_dict)
best_guess = np.argmax(pred) # highest-rated class
来源：https://stackoverflow.com/questions/46104783/making-predictions-with-tensorflow