def init_dataset(X_train_orig, Y_train_orig, X_test_orig, Y_test_orig):
X_train = X_train_orig / 255.
X_test = X_test_orig / 255.
Y_train = cnn_utils.convert_to_one_hot(Y_train_orig, 6).T
Y_test = cnn_utils.convert_to_one_hot(Y_test_orig, 6).T
# print ("number of training examples = " + str(X_train.shape[0]))
# print ("number of test examples = " + str(X_test.shape[0]))
# print ("X_train shape: " + str(X_train.shape))
# print ("Y_train shape: " + str(Y_train.shape))
# print ("X_test shape: " + str(X_test.shape))
# print ("Y_test shape: " + str(Y_test.shape))
return X_train, Y_train, X_test, Y_test
def main():
# Loading the data (signs)
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset(
)
# Example of a picture
index = 6
plt.imshow(X_train_orig[index])
print("y = " + str(np.squeeze(Y_train_orig[:, index])))
if DISPLAY: plt.show()
X_train = X_train_orig / 255.
X_test = X_test_orig / 255.
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T
print("number of training examples = " + str(X_train.shape[0]))
print("number of test examples = " + str(X_test.shape[0]))
print("X_train shape: " + str(X_train.shape))
print("Y_train shape: " + str(Y_train.shape))
print("X_test shape: " + str(X_test.shape))
print("Y_test shape: " + str(Y_test.shape))
# tf.reset_default_graph()
# with tf.Session() as sess:
# np.random.seed(1)
# X, Y = create_placeholders(64, 64, 3, 6)
# parameters = initialize_parameters()
# Z3 = forward_propagation(X, parameters)
# init = tf.global_variables_initializer()
# sess.run(init)
# a = sess.run(Z3, {X: np.random.randn(2, 64, 64, 3), Y: np.random.randn(2, 6)})
# print("Z3 = " + str(a))
#
# tf.reset_default_graph()
#
# with tf.Session() as sess:
# np.random.seed(1)
# X, Y = create_placeholders(64, 64, 3, 6)
# parameters = initialize_parameters()
# Z3 = forward_propagation(X, parameters)
# cost = compute_cost(Z3, Y)
# init = tf.global_variables_initializer()
# sess.run(init)
# a = sess.run(cost, {X: np.random.randn(4, 64, 64, 3), Y: np.random.randn(4, 6)})
# print("cost = " + str(a))
_, _, parameters = model(X_train,
Y_train,
X_test,
Y_test,
learning_rate=0.0015,
num_epochs=150)
def train_model(image_path, learning_rate=0.001, num_epoch=100, batch_size=5):
X_train = np.load(image_path + ".npy")
Y_train = np.load(image_path + ".npy")
X_train = X_train / 255
#Creating one-hot for label
print(Y_train.shape)
print(Y_train[0][1])
Y_train = convert_to_one_hot(Y_train, 10).T
n_Y = Y_train.shape[1]
x = tf.placeholder(tf.float32, [None, 227, 227, 3])
Y = tf.placeholder(tf.float32, [None, n_Y])
keep_prob = tf.placeholder(tf.float32)
model = AlexNet(x, keep_prob, 10, [])
score = model.fc8
cost = tf.reduce_mean(
(tf.nn.softmax_cross_entropy_with_logits_v2(logits=score, labels=Y)))
softmax = tf.nn.softmax(score)
costs = []
optimizer = tf.train.AdamOptimizer(
learning_rate=learning_rate).minimize(cost)
init = tf.global_variables_initializer()
[m, nH, nW, nCH] = X_train.shape
seed = 0
#tf.reset_default_graph()
with tf.Session() as sess:
sess.run(init)
sess.run(model.load_initial_weights(sess))
for i in range(num_epoch):
minibatch_cost = 0
num_minibatch = int(m / batch_size) # number of batches
seed = seed + 1
minibatches = random_mini_batches(X_train, Y_train, batch_size,
seed)
for minibatch in minibatches:
X_batches, Y_batches = minibatch
#Use cost and optimizer to run
_, c = sess.run([optimizer, cost], {
x: X_batches,
Y: Y_batches,
keep_prob: 1
})
minibatch_cost += c
minibatch_cost = minibatch_cost / num_minibatch
if i % 5 == 0:
print("Cost===>" + str(c))
costs.append(c)
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('epoch')
plt.title('learning_rate' + str(learning_rate))
plt.show()
import os
import tensorflow as tf
from tensorflow.python.framework import ops
try:
from cnn_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
except ImportError:
raise ImportError('The file is not found. Please check the file name!')
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# Loading the data (signs)
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
X_train = X_train_orig / 255.
X_test = X_test_orig / 255.
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T
print("number of training examples = " + str(X_train.shape[0]))
print("number of test examples = " + str(X_test.shape[0]))
print("X_train shape: " + str(X_train.shape))
print("Y_train shape: " + str(Y_train.shape))
print("X_test shape: " + str(X_test.shape))
print("Y_test shape: " + str(Y_test.shape))
def create_placeholders(n_H0, n_W0, n_C0, n_y):
"""
Creates the placeholders for the tensorflow session.
Arguments:
n_H0 -- scalar, height of an input image
train_accuracy = accuracy.eval({X: X_train, Y: Y_train})
test_accuary = accuracy.eval({X: X_test, Y: Y_test})
print("训练集准确度：" + str(train_accuracy))
print("测试集准确度：" + str(test_accuary))
return (train_accuracy, test_accuary, parameters)
if __name__ == '__main__':
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = cnn_utils.load_dataset(
)
index = 8
plt.imshow(X_train_orig[index])
plt.show()
print("y = " + str(np.squeeze(Y_train_orig[:, index])))
X_train = X_train_orig / 255.
X_test = X_test_orig / 255.
Y_train = cnn_utils.convert_to_one_hot(Y_train_orig, 6).T
Y_test = cnn_utils.convert_to_one_hot(Y_test_orig, 6).T
print("number of training examples = " + str(X_train.shape[0]))
print("number of test examples = " + str(X_test.shape[0]))
print("X_train shape: " + str(X_train.shape))
print("Y_train shape: " + str(Y_train.shape))
print("X_test shape: " + str(X_test.shape))
print("Y_test shape: " + str(Y_test.shape))
conv_layers = {}
_, _, parameters = model(X_train, Y_train, X_test, Y_test, num_epochs=150)
plt.xlabel('iterations (per tens)')
plt.ylabel('cost')
plt.title('Learning rate =' + str(learning_rate))
plt.show()
# 计算预测的正确率
predict_op = tf.argmax(Z3, 1)
correct_prediction = tf.equal(predict_op, tf.argmax(Y, 1))
# 计算在train_Set 和 test_Set上的准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy)
train_accuracy = accuracy.eval({X: train_X, Y: train_Y})
test_accuracy = accuracy.eval({X: train_X, Y: train_Y})
print('Train Accuracy:', train_accuracy)
print('Test Accuracy:', test_accuracy)
return train_accuracy, test_accuracy, parameters
if __name__ == '__main__':
train_X, train_Y, test_X, test_Y, classes = cnn_utils.load_dataset()
# 归一化数据集
train_X = train_X / 255
test_X = test_X / 255
# 将标签转换为one-hot编码
train_Y = cnn_utils.convert_to_one_hot(train_Y, 6).T
test_Y = cnn_utils.convert_to_one_hot(test_Y, 6).T
_, _, parameters = model(train_X, train_Y, test_X, test_Y, num_epochs=100)
# init = tf.global_variables_initializer()
# sess_test.run(init)
# a = sess_test.run(cost,{X:np.random.randn(4,64,64,3),Y:np.random.randn(4,6)})
# print('cost = '+str(a))
#
# sess_test.close()
##2.1.4构建模型
X_train_orig , Y_train_orig , X_test_orig , Y_test_orig , classes = cnn_utils.load_dataset()
# index = 6
# plt.imshow(X_train_orig[index])
# print ("y = " + str(np.squeeze(Y_train_orig[:, index])))
X_train = X_train_orig/255.
X_test = X_test_orig/255.
Y_train = cnn_utils.convert_to_one_hot(Y_train_orig, 6).T
Y_test = cnn_utils.convert_to_one_hot(Y_test_orig, 6).T
print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))
conv_layers = {}
def model(X_train,Y_train,X_test,Y_test,learning_rate = 0.009,
num_epochs = 100,minibatch_size = 64,print_cost = True,isPlot =True):
'''
使用TensorFlow实现三层的卷积神经网络
conv2d->relu->maxpool->>conv2d->relu->maxpool->flatten->fullyconnnected
:param X_train: 训练数据,维度为(None,64,64,3)
