Dumping numpy arrays into neural network - csv

Question

I am trying to solve the titanic machine learning challenge from kaggle using a neural network. I removed most of the irrelevant data and converted the useful data into a 2D numpy array while the survival is converted into a 1D numpy array. For some reason it throws an error saying dimension 0 in both shape must be equal, I've been trying to solve it for quite a while and I hope that you guys can help out.
TensorFlowNumpy.py
import tensorflow as tf
def numpy2tensor(numpy):
sess = tf.Session()
with sess.as_default():
return tf.constant(numpy)
def tensor2numpy(tensor):
sess = tf.Session()
with sess.as_default():
return tensor.eval()
Dataset.py
import pandas
import numpy as np
dataset = pandas.read_csv('train.csv')
dataset2= dataset.drop(['PassengerId','Survived','Name','Ticket','Fare','Cabin','Embarked'],axis=1)
dataset3= dataset2.fillna(0)
survive = pandas.read_csv('train.csv')
survival = np.float32(survive.Survived)
dataset4 = np.float32(dataset3)
MainCode.py
import tensorflow as tf
import numpy
from dataset import dataset4,survival
from sklearn.model_selection import train_test_split
from TensorFlowNumpy import numpy2tensor
train_x,test_x,train_y,test_y = train_test_split(dataset4,survival,test_size
= 0.2)
tensor_train_x = numpy2tensor(train_x)
tensor_train_y = numpy2tensor(train_y)
tensor_test_x = numpy2tensor(test_x)
tensor_test_y = numpy2tensor(test_y)
n_nodes_hl1 = 10
n_nodes_hl2 = 10
n_classes = 2
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
def neural_network_model(data):
hidden_1_layer = {'weights':tf.Variable(tf.random_normal([5,
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]))}
output_layer = {'weights':tf.Variable(tf.random_normal([n_nodes_hl2,
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)
output = tf.matmul(l2,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=tensor_train_y))
optimizer1 = tf.train.GradientDescentOptimizer(0.001).minimize(cost)
hm_epochs = 100
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(hm_epochs):
epoch_loss = 0
_, c = sess.run([optimizer1, cost], feed_dict={x:tensor_train_x,
y:tensor_train_y})
epoch_loss += c
print('Epoch', epoch+1, '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(tensor_train_x)

Answer 1

I have faced this error several times, problem is obviously in our code. I didn't look through your code thoroughly as i am leaving for the day, but i smell that your dependent variable/ output variable shape is [1,712] which should be [712,1] so some where in the code try to fix it. Basically what it meant is you are having one row with 712 columns but it should be 712 rows with 1 column(output). Please mark this as answer if it helps. Ping me tomorrow if problem still exists. I will take a look at it.