Я пытаюсь напечатать матрицу путаницы, но получаю ее частичную печать (с ...), как я могу заставить ее печатать все значения моего измерения = 100?
, чтобы напечататьТочность в процессе обучения и печать матрицы путаницы в конце.
пробовал оба способа печати матрицы путаницы: sklearn & tenorflow, но оба не выводят полный вывод матрицы
Матрица путаницы:
0 0 0 ... 0 0 0
1 0 0 ... 0 0 0
0 1 0 ... 0 0 0
...
0 1 0 ... 0 0 0
0 0 1 ... 0 0 0
0 0 0 ... 0 0 0
Я новичок в глубоком изучении и пытаюсь реализовать пример тензорного потока в игре fizzbuzz.учитывая массив чисел от 1 до 100, нужно напечатать: - fizz for all i mod 3 == 0 - buzz для всех i mod 5 == 0 - fizzbuzz для всех i mod 15 == 0
import numpy as np
import tensorflow as tf
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
NUM_DIGITS = 10
VICTOR_SIZE = 4
# Represent each input by an array of its binary digits.
def binary_encode(i, num_digits):
# print (np.array([i >> d & 1 for d in range(num_digits)]))
return np.array([i >> d & 1 for d in range(num_digits)])
# One-hot encode the desired outputs: [number, "fizz", "buzz", "fizzbuzz"]
def fizz_buzz_encode(i):
if i % 15 == 0: return np.array([0, 0, 0, 1])
elif i % 5 == 0: return np.array([0, 0, 1, 0])
elif i % 3 == 0: return np.array([0, 1, 0, 0])
else: return np.array([1, 0, 0, 0])
# Our goal is to produce fizzbuzz for the numbers 1 to 100. So it would be
# unfair to include these in our training data. Accordingly, the training data
# corresponds to the numbers 101 to (2 ** NUM_DIGITS - 1).
trX = np.array([binary_encode(i, NUM_DIGITS) for i in range(101, 2 ** NUM_DIGITS)])
trY = np.array([fizz_buzz_encode(i) for i in range(101, 2 ** NUM_DIGITS)])
# We'll want to randomly initialize weights.
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
# Our model is a standard 1-hidden-layer multi-layer-perceptron with ReLU
# activation. The softmax (which turns arbitrary real-valued outputs into
# probabilities) gets applied in the cost function.
def model(X, w_h, w_o):
h = tf.nn.relu(tf.matmul(X, w_h))
return tf.matmul(h, w_o)
# Our variables. The input has width NUM_DIGITS, and the output has width 4.
X = tf.placeholder("float", [None, NUM_DIGITS])
Y = tf.placeholder("float", [None, VICTOR_SIZE])
# How many units in the hidden layer.
NUM_HIDDEN = 100
# Initialize the weights.
w_h = init_weights([NUM_DIGITS, NUM_HIDDEN])
w_o = init_weights([NUM_HIDDEN, VICTOR_SIZE])
# Predict y given x using the model.
py_x = model(X, w_h, w_o)
# We'll train our model by minimizing a cost function.
#cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) ##TODO
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = py_x, labels=Y))
train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) #consider adam optimizer
# And we'll make predictions by choosing the largest output.
predict_op = tf.argmax(py_x, 1)
# Finally, we need a way to turn a prediction (and an original number)
# into a fizz buzz output
def fizz_buzz(i, prediction):
return [str(i), "fizz", "buzz", "fizzbuzz"][prediction]
def analyze_results(actual, predicted):
#print("\nAnalyze results.....................................................")
#print("Accuracy Score :", accuracy_score(actual, predicted, normalize=False))
print("Confusion Matrix :\n", confusion_matrix(actual, predicted))
# to evaluate the accuracy of a classification (performance of classifier)
# print("Report :", classification_report(actual, predicted))
BATCH_SIZE = 128 #consider smaller batches
# Launch the graph in a session
with tf.Session() as sess:
##TODO
#tf.initialize_all_variables().run()
sess.run(tf.global_variables_initializer())
for epoch in range(10000):
# Shuffle the data before each training iteration.
p = np.random.permutation(range(len(trX))) #random order of input
trX, trY = trX[p], trY[p]
# Train in batches of 128 inputs.
for start in range(0, len(trX), BATCH_SIZE):
end = start + BATCH_SIZE
sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})
# And print the current accuracy on the training data.
print("current accuracy on the training data: ", epoch, np.mean(np.argmax(trY, axis=1) ==
sess.run(predict_op, feed_dict={X: trX, Y: trY})))
# And now for some fizz buzz
numbers = np.arange(1, 101)
teX = np.transpose(binary_encode(numbers, NUM_DIGITS))
teY = sess.run(predict_op, feed_dict={X: teX})
output = np.vectorize(fizz_buzz)(numbers, teY)
print("\nOutput = ", output)
# print("\naccuracy = ", np.mean(np.argmax(trY, axis=1) == sess.run(predict_op, feed_dict={X: trX, Y: trY})))
# conf_matrix = tf.confusion_matrix(labels=numbers, predictions=teY) #, num_classes=None, dtype=None, name=None)
# print("\nConfusion Matrix :\n", sess.run(conf_matrix, feed_dict={X: trX, Y: trY}))
# print("\nConfusion Matrix :\n", confusion_matrix(numbers, teY)) # Using Sklearn
analyze_results(numbers, teY)