import pandas as pd
import tensorflow as tf
import numpy as np

###################################
#Treinando
###################################
train = pd.read_csv("../input/train.csv").as_matrix()
print("Training set has {0[0]} rows and {0[1]} columns".format(train.shape))
print(pd.get_dummies(train[0:100,0]))


#################################
#Construindo o modelo
#################################
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b

y_ = tf.placeholder(tf.float32, [None, 10])

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
###################################
#Treinando
###################################
for _ in range(1):
   batch_xs = train[:,1:]
   batch_ys = pd.get_dummies(train[:,0]).as_matrix()
   sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
   
exit()

#################################
#Testando
#################################
test  = pd.read_csv("../input/test.csv")
print("Test set has {0[0]} rows and {0[1]} columns".format(test.shape))
# Any files you write to the current directory get shown as outputs