本文整理匯總了Python中input_data.read_data_sets方法的典型用法代碼示例。如果您正苦於以下問題:Python input_data.read_data_sets方法的具體用法?Python input_data.read_data_sets怎麽用?Python input_data.read_data_sets使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類input_data
的用法示例。
在下文中一共展示了input_data.read_data_sets方法的6個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: __init__
# 需要導入模塊: import input_data [as 別名]
# 或者: from input_data import read_data_sets [as 別名]
def __init__(self):
self.mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
self.n_samples = self.mnist.train.num_examples
self.n_hidden = 500
self.n_z = 20
self.batchsize = 100
self.images = tf.placeholder(tf.float32, [None, 784])
image_matrix = tf.reshape(self.images,[-1, 28, 28, 1])
z_mean, z_stddev = self.recognition(image_matrix)
samples = tf.random_normal([self.batchsize,self.n_z],0,1,dtype=tf.float32)
guessed_z = z_mean + (z_stddev * samples)
self.generated_images = self.generation(guessed_z)
generated_flat = tf.reshape(self.generated_images, [self.batchsize, 28*28])
self.generation_loss = -tf.reduce_sum(self.images * tf.log(1e-8 + generated_flat) + (1-self.images) * tf.log(1e-8 + 1 - generated_flat),1)
self.latent_loss = 0.5 * tf.reduce_sum(tf.square(z_mean) + tf.square(z_stddev) - tf.log(tf.square(z_stddev)) - 1,1)
self.cost = tf.reduce_mean(self.generation_loss + self.latent_loss)
self.optimizer = tf.train.AdamOptimizer(0.001).minimize(self.cost)
# encoder
示例2: read_data_sets
# 需要導入模塊: import input_data [as 別名]
# 或者: from input_data import read_data_sets [as 別名]
def read_data_sets():
basepath = '/'.join(__file__.split('/')[:-1])
import input_data
data = input_data.read_data_sets(basepath+'/bin', one_hot=True)
import os
import numpy as np
if not os.path.isfile(basepath+'/bin/permutation.npy'):
indices = np.random.permutation(28**2)
os.makedirs(basepath+'/bin', exist_ok=True)
np.save(basepath+'/bin/permutation.npy', indices)
else:
indices = np.load(basepath+'/bin/permutation.npy')
data.train.images[:,:] = data.train.images[:,indices]
data.validation.images[:,:] = data.validation.images[:,indices]
data.test.images[:,:] = data.test.images[:,indices]
return data
示例3: train_and_save_model
# 需要導入模塊: import input_data [as 別名]
# 或者: from input_data import read_data_sets [as 別名]
def train_and_save_model(self, data_location, save_location):
# Our training data
mnist = input_data.read_data_sets(data_location, one_hot=True)
for i in range(20000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = self.accuracy.eval(feed_dict={
self.x:batch[0], self.y_: batch[1], self.keep_prob: 1.0
})
print("step %d, training accuracy %g"%(i, train_accuracy))
self.train_step.run(feed_dict={self.x: batch[0], self.y_: batch[1], self.keep_prob: 0.5})
# Saves path
save_path = saver.save(sess, save_location)
print("Model saved in file: ", save_path)
# Loads saved model
示例4: train_and_save_model
# 需要導入模塊: import input_data [as 別名]
# 或者: from input_data import read_data_sets [as 別名]
def train_and_save_model(self):
# Our training data
mnist = input_data.read_data_sets('../data/MNIST_digits', one_hot=True)
for i in range(20000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = self.accuracy.eval(feed_dict={
self.x:batch[0], self.y_: batch[1], self.keep_prob: 1.0
})
print("step %d, training accuracy %g"%(i, train_accuracy))
self.train_step.run(feed_dict={self.x: batch[0], self.y_: batch[1], self.keep_prob: 0.5})
# Saves path
save_path = saver.save(sess, "../trained_models/mnist_digits.ckpt")
print("Model saved in file: ", save_path)
# Loads saved model
示例5: main
# 需要導入模塊: import input_data [as 別名]
# 或者: from input_data import read_data_sets [as 別名]
def main(_):
# Import data
mnist = input_data.read_data_sets('data', one_hot=True, validation_size=0)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
W_fc1 = weight_variable([28*28, 1000])
b_fc1 = bias_variable([1000])
h_fc1 = tf.nn.relu(tf.matmul(x, W_fc1) + b_fc1)
W_fc2 = weight_variable([1000, 1000])
b_fc2 = bias_variable([1000])
h_fc2 = tf.nn.relu(tf.matmul(h_fc1, W_fc2) + b_fc2)
W_fc3 = weight_variable([1000, 10])
b_fc3 = bias_variable([10])
out = tf.matmul(h_fc2, W_fc3) + b_fc3
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=out))
train_step = cocob_optimizer.COCOB().minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(out, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(600*40):
batch = mnist.train.next_batch(100)
if i % 600 == 0:
test_batch_size = 10000
batch_num = int(mnist.train.num_examples / test_batch_size)
train_loss = 0
for j in range(batch_num):
train_loss += cross_entropy.eval(feed_dict={x: mnist.train.images[test_batch_size*j:test_batch_size*(j+1), :],
y_: mnist.train.labels[test_batch_size*j:test_batch_size*(j+1), :]})
train_loss /= batch_num
test_err = 1-accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels})
print('epoch %d, training cost %g, test error %g ' % (i/600, train_loss, test_err))
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
示例6: main
# 需要導入模塊: import input_data [as 別名]
# 或者: from input_data import read_data_sets [as 別名]
def main():
mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)
data = mnist.train.next_batch(8000)
train_x = data[0]
Y = data[1]
train_y = (np.arange(np.max(Y) + 1) == Y[:, None]).astype(int)
mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)
tb = mnist.train.next_batch(2000)
Y_test = tb[1]
X_test = tb[0]
# 0.00002-92
# 0.000005-92, 93 when 200000 190500
d1 = Digit_Recognizer_LR.model(train_x.T, train_y.T, Y, X_test.T, Y_test, num_iters=1500, alpha=0.05,
print_cost=True)
w_LR = d1["w"]
b_LR = d1["b"]
d2 = Digit_Recognizer_NN.model_nn(train_x.T, train_y.T, Y, X_test.T, Y_test, n_h=100, num_iters=1500, alpha=0.05,
print_cost=True)
dims = [784, 100, 80, 50, 10]
d3 = Digit_Recognizer_DL.model_DL(train_x.T, train_y.T, Y, X_test.T, Y_test, dims, alpha=0.5, num_iterations=1100,
print_cost=True)
cap = cv2.VideoCapture(0)
while (cap.isOpened()):
ret, img = cap.read()
img, contours, thresh = get_img_contour_thresh(img)
ans1 = ''
ans2 = ''
ans3 = ''
if len(contours) > 0:
contour = max(contours, key=cv2.contourArea)
if cv2.contourArea(contour) > 2500:
# print(predict(w_from_model,b_from_model,contour))
x, y, w, h = cv2.boundingRect(contour)
# newImage = thresh[y - 15:y + h + 15, x - 15:x + w +15]
newImage = thresh[y:y + h, x:x + w]
newImage = cv2.resize(newImage, (28, 28))
newImage = np.array(newImage)
newImage = newImage.flatten()
newImage = newImage.reshape(newImage.shape[0], 1)
ans1 = Digit_Recognizer_LR.predict(w_LR, b_LR, newImage)
ans2 = Digit_Recognizer_NN.predict_nn(d2, newImage)
ans3 = Digit_Recognizer_DL.predict(d3, newImage)
x, y, w, h = 0, 0, 300, 300
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(img, "Logistic Regression : " + str(ans1), (10, 320),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(img, "Shallow Network : " + str(ans2), (10, 350),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(img, "Deep Network : " + str(ans3), (10, 380),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.imshow("Frame", img)
cv2.imshow("Contours", thresh)
k = cv2.waitKey(10)
if k == 27:
break