I'm a newbie to ML and learning TF through this tutorial -
In the following code, I can calculate epoch loss but not accuracy.
import tensorflow as tf
from wordsnlp import create_feature_sets_and_labels
import numpy as np
train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt')
n_nodes_hl1 = 500
n_classes = 2
batch_size = 100
x = tf.placeholder('float',[None,len(train_x[0])])
y = tf.placeholder('float')
#(input_data*weights) + biases
def neural_network_model(data):
hidden_1_layer = {'weights': tf.Variable(tf.random_normal([len(train_x[0]),n_nodes_hl1])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}
return output
def neural_network_model(data):
hidden_1_layer = {'weights': tf.Variable(tf.random_normal([len(train_x[0]),n_nodes_hl1])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}
hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1,n_nodes_hl2])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))}
hidden_3_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2,n_nodes_hl3])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl3]))}
output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl3,n_classes])),
'biases': tf.Variable(tf.random_normal([n_classes]))}
l1= tf.add(tf.matmul(data, hidden_1_layer['weights']) , hidden_1_layer['biases'])
l1 = tf.nn.relu(l1)
l2= tf.add(tf.matmul(l1, hidden_2_layer['weights']) , hidden_2_layer['biases'])
l1 = tf.nn.relu(l2)
l3= tf.add(tf.matmul(l2, hidden_2_layer['weights']) , hidden_2_layer['biases'])
l1 = tf.nn.relu(l3)
output = tf.matmul(l3, output_layer['weights']) + output_layer['biases']
return output
def train_neural_network(x):
prediction = neural_network_model(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
hm_epochs = 10
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for epoch in range(hm_epochs):
epoch_loss=0
i=0
while i < len(train_x):
start = i
end = i + batch_size
batch_x = np.array(train_x[start:end])
batch_y = np.array(train_y[start:end])
_,c = sess.run([optimizer,cost] , feed_dict = {x: batch_x , y : batch_y})
epoch_loss+= c
i+= batch_size
print("Epoch",epoch , 'completed out of ' ,hm_epochs, ' loss: ', epoch_loss )
correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy: ', accuracy.eval({x:test_x , y: test_y}))
train_neural_network(x)
The error which i'm getting for this code(I simplified) while i'm calculating accuracy is :
ValueError: Cannot feed value of shape (423,) for Tensor
'Placeholder:0', which has shape '(?, 423)'
Can you please point out what's the problem is?
Thanks in advance.
First of all, your code is incomplete, check neural_network_model function.
Anyways the following code works. For now, I have just used one network layer, you can add more layers in your neural_network_model function. Making sure that n_classes and the size of output in neural_network_model function is same.
For now run the below code, and then later update neural_network_model function.
import tensorflow as tf
import numpy as np
import random
import nltk
from nltk.tokenize import word_tokenize
import numpy as np
import random
import pickle
from collections import Counter
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
hm_lines = 100000
def create_lexicon(pos,neg):
lexicon = []
with open(pos,'r') as f:
contents = f.readlines()
for l in contents[:hm_lines]:
all_words = word_tokenize(l)
lexicon += list(all_words)
with open(neg,'r') as f:
contents = f.readlines()
for l in contents[:hm_lines]:
all_words = word_tokenize(l)
lexicon += list(all_words)
lexicon = [lemmatizer.lemmatize(i) for i in lexicon]
w_counts = Counter(lexicon)
l2 = []
for w in w_counts:
#print(w_counts[w])
if 1000 > w_counts[w] > 50:
l2.append(w)
print(len(l2))
return l2
def sample_handling(sample,lexicon,classification):
featureset = []
with open(sample,'r') as f:
contents = f.readlines()
for l in contents[:hm_lines]:
current_words = word_tokenize(l.lower())
current_words = [lemmatizer.lemmatize(i) for i in current_words]
features = np.zeros(len(lexicon))
for word in current_words:
if word.lower() in lexicon:
index_value = lexicon.index(word.lower())
features[index_value] += 1
features = list(features)
featureset.append([features,classification])
return featureset
def create_feature_sets_and_labels(pos,neg,test_size = 0.1):
lexicon = create_lexicon(pos,neg)
features = []
features += sample_handling('pos.txt',lexicon,[1,0])
features += sample_handling('neg.txt',lexicon,[0,1])
random.shuffle(features)
features = np.array(features)
testing_size = int(test_size*len(features))
train_x = list(features[:,0][:-testing_size])
train_y = list(features[:,1][:-testing_size])
test_x = list(features[:,0][-testing_size:])
test_y = list(features[:,1][-testing_size:])
return train_x,train_y,test_x,test_y
train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt')
n_nodes_hl1 = 2
n_classes = 2
batch_size = 100
x = tf.placeholder('float',[None,len(train_x[0])])
y = tf.placeholder('float')
#(input_data*weights) + biases
def neural_network_model(data):
hidden_1_layer = {'weights': tf.Variable(tf.random_normal([len(train_x[0]),n_nodes_hl1])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}
output = tf.matmul(data,hidden_1_layer['weights']) + hidden_1_layer['biases']
return output
def train_neural_network(x):
prediction = neural_network_model(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
hm_epochs = 1
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for epoch in range(hm_epochs):
epoch_loss=0
i=0
while i < len(train_x):
start = i
end = i + batch_size
batch_x = np.array(train_x[start:end])
batch_y = np.array(train_y[start:end])
_,c = sess.run([optimizer,cost] , feed_dict = {x: batch_x , y : batch_y})
epoch_loss+= c
i+= batch_size
print("Epoch",epoch , 'completed out of ' ,hm_epochs, ' loss: ', epoch_loss )
correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy: ', accuracy.eval({x:test_x , y: test_y}))
train_neural_network(x)
Note: The code has flaws at other levels, but that is not the point of this question, I took the missing functions from the place you pointed
Edit 2:
I guess I should not be encouraging you with your silly mistakes, this is last time I am fixing things. You have again just messed up in the same function. You must first go through your code completely before posting it to stack-overflow, so that you are sure you are asking the correct problem you encountering, and not a side silly mistake.
import tensorflow as tf
import numpy as np
import random
import nltk
from nltk.tokenize import word_tokenize
import numpy as np
import random
import pickle
from collections import Counter
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
hm_lines = 100000
def create_lexicon(pos,neg):
lexicon = []
with open(pos,'r') as f:
contents = f.readlines()
for l in contents[:hm_lines]:
all_words = word_tokenize(l)
lexicon += list(all_words)
with open(neg,'r') as f:
contents = f.readlines()
for l in contents[:hm_lines]:
all_words = word_tokenize(l)
lexicon += list(all_words)
lexicon = [lemmatizer.lemmatize(i) for i in lexicon]
w_counts = Counter(lexicon)
l2 = []
for w in w_counts:
#print(w_counts[w])
if 1000 > w_counts[w] > 50:
l2.append(w)
print(len(l2))
return l2
def sample_handling(sample,lexicon,classification):
featureset = []
with open(sample,'r') as f:
contents = f.readlines()
for l in contents[:hm_lines]:
current_words = word_tokenize(l.lower())
current_words = [lemmatizer.lemmatize(i) for i in current_words]
features = np.zeros(len(lexicon))
for word in current_words:
if word.lower() in lexicon:
index_value = lexicon.index(word.lower())
features[index_value] += 1
features = list(features)
featureset.append([features,classification])
return featureset
def create_feature_sets_and_labels(pos,neg,test_size = 0.1):
lexicon = create_lexicon(pos,neg)
features = []
features += sample_handling('pos.txt',lexicon,[1,0])
features += sample_handling('neg.txt',lexicon,[0,1])
random.shuffle(features)
features = np.array(features)
testing_size = int(test_size*len(features))
train_x = list(features[:,0][:-testing_size])
train_y = list(features[:,1][:-testing_size])
test_x = list(features[:,0][-testing_size:])
test_y = list(features[:,1][-testing_size:])
return train_x,train_y,test_x,test_y
train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt')
n_nodes_hl1 = 2
n_classes = 2
batch_size = 100
x = tf.placeholder('float',[None,len(train_x[0])])
y = tf.placeholder('float')
import tensorflow as tf
import numpy as np
train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt')
n_nodes_hl1 = 4
n_nodes_hl2 = 3
n_nodes_hl3 = 2
n_classes = 2
batch_size = 100
x = tf.placeholder('float',[None,len(train_x[0])])
y = tf.placeholder('float')
def neural_network_model(data):
hidden_1_layer = {'weights': tf.Variable(tf.random_normal([len(train_x[0]),n_nodes_hl1])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}
hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1,n_nodes_hl2])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))}
hidden_3_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2,n_nodes_hl3])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl3]))}
output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl3,n_classes])),
'biases': tf.Variable(tf.random_normal([n_classes]))}
l1= tf.add(tf.matmul(data, hidden_1_layer['weights']) , hidden_1_layer['biases'])
l1 = tf.nn.relu(l1)
l2= tf.add(tf.matmul(l1, hidden_2_layer['weights']) , hidden_2_layer['biases'])
l2 = tf.nn.relu(l2)
l3= tf.add(tf.matmul(l2, hidden_3_layer['weights']) , hidden_3_layer['biases'])
l3 = tf.nn.relu(l3)
output = tf.matmul(l3, output_layer['weights']) + output_layer['biases']
return output
def train_neural_network(x):
prediction = neural_network_model(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
hm_epochs = 10
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for epoch in range(hm_epochs):
epoch_loss=0
i=0
while i < len(train_x):
start = i
end = i + batch_size
batch_x = np.array(train_x[start:end])
batch_y = np.array(train_y[start:end])
_,c = sess.run([optimizer,cost] , feed_dict = {x: batch_x , y : batch_y})
epoch_loss+= c
i+= batch_size
print("Epoch",epoch , 'completed out of ' ,hm_epochs, ' loss: ', epoch_loss )
correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy: ', accuracy.eval({x:test_x , y: test_y}))
train_neural_network(x)
