Q:

How to predict label only instead of probability

When i am predicting the output i am getting probability of each label (Example Output = [0.002345, 0.997655] ) instead of correct label. Is there any way to get the label only instead of their probability? 
import tflearn    
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
from tflearn.data_utils import load_csv
import numpy as np

data, labels = load_csv("C:/Users/Animkush/Desktop/train.csv", 
categorical_labels=True, n_classes=2, target_column=30)

def preprocess(data, col):
    for id in sorted(col, reverse=True):
    [r.pop(id) for r in data]

    return np.array(data, dtype=np.float32)

ignore = [0]

data = preprocess(data, ignore)

network = input_data(shape=[None, 28], name="input")

network = tflearn.fully_connected(network, 32, activation="relu")
network = tflearn.fully_connected(network, 64, activation="relu")
network = tflearn.fully_connected(network, 128, activation="relu")
network = tflearn.fully_connected(network, 64, activation="relu")
network = tflearn.fully_connected(network, 32, activation="relu")

network = dropout(network, 0.8)

network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy', name='targets')

model = tflearn.DNN(network)

model.fit(data, labels, n_epoch=100, batch_size=500, show_metric=True)

p = np.array([[-3.043540624,-3.157307121,1.08846278,2.288643618,1.35980513,-1.064822523,0.325574266,-0.067793653,-0.270952836,-0.838586565,-0.414575448,-0.50314086,0.676501545,-1.692028933,2.000634839,0.666779696,0.599717414,1.725321007,0.28334483,2.102338793,0.661695925,0.435477209,1.375965743,-0.293803153,0.279798032,-0.145361715,-0.252773123,0.035764225]])

print(model.predict(p))

A:

The Numpy method argmax is routinely used for this purpose:
import numpy as np

p = np.array([0.002345, 0.997655]) # array of probabilities, as per your example
y = np.argmax(p) # hard class
y
# 1

# another example:
p = np.array([0.78, 0.22])
y = np.argmax(p)
y
# 0

