def __init__():
# Loading the dataset
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = utils.load_dataset(
'dataset/signs/test_signs.h5', 'dataset/signs/train_signs.h5')
# Flatten the image dataset, then normalize it by dividing by 255. On top of that, you will convert
# each label to a one-hot vector as shown in Figure 1. Run the cell below to do so.
# Flatten the training and test images
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
# Normalize image vectors
X_train = X_train_flatten / 255.
X_test = X_test_flatten / 255.
# Convert training and test labels to one hot matrices
Y_train = utils.convert_to_one_hot(Y_train_orig, 6)
Y_test = utils.convert_to_one_hot(Y_test_orig, 6)
print("number of training examples = " + str(X_train.shape[1]))
print("number of test examples = " + str(X_test.shape[1]))
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))
X, Y = utils.create_placeholders(12288, 6)
print("X = " + str(X))
print("Y = " + str(Y))
parameters = model(X_train, Y_train, X_test, Y_test)
print('PARAMETERS', parameters)
def main():
print("result = " + str(linear_function()))
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
# index = 36
# plt.imshow(X_train_orig[index])
# print("y = " + str(np.squeeze(Y_train_orig[:, index])))
# plt.show()
# model-part
# Flatten the training and test images
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
# Normalize image vectors
X_train = X_train_flatten / 255.
X_test = X_test_flatten / 255.
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6)
Y_test = convert_to_one_hot(Y_test_orig, 6)
#
learned_parameters = model(X_train, Y_train, X_test, Y_test, num_epochs=1000)
return learned_parameters
def main():
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset(
)
# Flatten the training and test images
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
# Normalize image vectors
X_train = X_train_flatten / 255.
X_test = X_test_flatten / 255.
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6)
Y_test = convert_to_one_hot(Y_test_orig, 6)
#
start_time = time.time()
learned_parameters = model(X_train,
Y_train,
X_test,
Y_test,
num_epochs=600)
print("--- %s seconds ---" % (time.time() - start_time))
return learned_parameters
def get_dataset():
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
index = 0
#plt.imshow(X_train_orig[index])
#print ("y = " + str(np.squeeze(Y_train_orig[:, index])))
# 扁平化
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
# 简单的归一化
X_train = X_train_flatten / 255.
X_test = X_test_flatten / 255.
# one hot编码
Y_train = convert_to_one_hot(Y_train_orig, 6)
Y_test = convert_to_one_hot(Y_test_orig, 6)
return X_train, X_test, Y_train, Y_test
def load():
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset(
)
# Flatten the training and test images
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
# Normalize image vectors
X_train = X_train_flatten / 255.
X_test = X_test_flatten / 255.
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6)
Y_test = convert_to_one_hot(Y_test_orig, 6)
print("number of training examples = " + str(X_train.shape[1]))
print("number of test examples = " + str(X_test.shape[1]))
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
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Accuracy:", 100 * accuracy.eval({
X: X_test,
Y: Y_test
}), "%")
pass
else:
print('No checkpoints found!')
pass
pass
tf.reset_default_graph()
# Loading the dataset
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
# Example of a picture
index = 0
plt.imshow(X_train_orig[index])
plt.show()
print("y = " + str(np.squeeze(Y_train_orig[:, index])))
# Flatten the training and test images
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
# Normalize image vectors
X_train = X_train_flatten / 255.
X_test = X_test_flatten / 255.
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6)
sess = tf.Session()
# Run the session to compute 'ones' (approx. 1 line)
ones = sess.run(ones)
# Close the session (approx. 1 line). See method 1 above.
sess.close()
### END CODE HERE ###
return ones
# # 2 - Building your first neural network in tensorflow
# Loading the dataset
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
def create_placeholders(n_x, n_y):
"""
Creates the placeholders for the tensorflow session.
Arguments:
n_x -- scalar, size of an image vector (num_px * num_px = 64 * 64 * 3 = 12288)
n_y -- scalar, number of classes (from 0 to 5, so -> 6)
Returns:
X -- placeholder for the data input, of shape [n_x, None] and dtype "float"
Y -- placeholder for the input labels, of shape [n_y, None] and dtype "float"
Tips:
- You will use None because it let's us be flexible on the number of examples you will for the placeholders.
def ones(shape):
"""
print(ones([3, 3]))
:param shape:
"""
ones = tf.ones(shape)
sess = tf.Session()
result = sess.run(ones)
sess.close()
return result
# (1080,64,64,3) (1,1080) (120,64,64,3) (1,120) [1 2 3 4 5]
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = tf_utils.load_dataset(
)
# 归一化数据
X_train = X_train_orig / 255
X_test = X_test_orig / 255
# reshape
X_train = X_train.reshape(X_train.shape[0], -1).T
X_test = X_test.reshape(X_test.shape[0], -1).T
# 转换为one-hot矩阵
Y_train = tf_utils.convert_to_one_hot(
Y_train_orig, 6) # 根据label进行独热编码 行=深度=6=[012345]， 列=训练样本数 =1080
Y_test = tf_utils.convert_to_one_hot(
Y_test_orig, 6) # 根据label进行独热编码 行=深度=6=[012345]， 列=训练样本数 =120
def main():
np.random.seed(1)
y_hat = tf.constant(36, name='y_hat') # Define y_hat constant. Set to 36.
y = tf.constant(39, name='y') # Define y. Set to 39
loss = tf.Variable((y - y_hat) ** 2, name='loss') # Create a variable for the loss
init = tf.global_variables_initializer() # When init is run later (session.run(init)),
# the loss variable will be initialized and ready to be computed
with tf.Session() as session: # Create a session and print the output
session.run(init) # Initializes the variables
print(session.run(loss)) # Prints the loss
a = tf.constant(2)
b = tf.constant(10)
c = tf.multiply(a, b)
print(c)
sess = tf.Session()
print(sess.run(c))
# Change the value of x in the feed_dict
x = tf.placeholder(tf.int64, name='x')
print(sess.run(2 * x, feed_dict={x: 3}))
sess.close()
""" TEST 1 """
print("result = " + str(linear_function()))
""" TEST 2 """
print("sigmoid(0) = " + str(sigmoid(0)))
print("sigmoid(12) = " + str(sigmoid(12)))
""" TEST 3 """
logits = sigmoid(np.array([0.2, 0.4, 0.7, 0.9]))
cost = cost_func(logits, np.array([0, 0, 1, 1]))
print("cost = " + str(cost))
""" TEST 4 """
labels = np.array([1, 2, 3, 0, 2, 1])
one_hot = one_hot_matrix(labels, C=4)
print("one_hot = " + str(one_hot))
""" TEST 5 """
print("ones = " + str(ones([3])))
# Loading the dataset
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
# Change the index below and run the cell to visualize some examples in the dataset.
# Example of a picture
index = 99
#plt.imshow(X_train_orig[index])
print("y = " + str(np.squeeze(Y_train_orig[:, index])))
# Flatten the training and test images
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
# Normalize image vectors
X_train = X_train_flatten / 255.
X_test = X_test_flatten / 255.
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6)
Y_test = convert_to_one_hot(Y_test_orig, 6)
print("number of training examples = " + str(X_train.shape[1]))
print("number of test examples = " + str(X_test.shape[1]))
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))
plt.show()
""" TEST 6 """
X, Y = create_placeholders(12288, 6)
print("X = " + str(X))
print("Y = " + str(Y))
""" TEST 7 """
tf.reset_default_graph()
with tf.Session() as sess:
parameters = initialize_parameters()
print("W1 = " + str(parameters["W1"]))
print("b1 = " + str(parameters["b1"]))
print("W2 = " + str(parameters["W2"]))
print("b2 = " + str(parameters["b2"]))
""" TEST 8 """
tf.reset_default_graph()
with tf.Session() as sess:
X, Y = create_placeholders(12288, 6)
parameters = initialize_parameters()
Z3 = forward_propagation(X, parameters)
print("Z3 = " + str(Z3))
""" TEST 9 """
tf.reset_default_graph()
with tf.Session() as sess:
X, Y = create_placeholders(12288, 6)
parameters = initialize_parameters()
Z3 = forward_propagation(X, parameters)
cost = compute_cost(Z3, Y)
print("cost = " + str(cost))
""" TEST 10 """
parameters = model(X_train, Y_train, X_test, Y_test)
""" TEST 11 """
## START CODE HERE ## (PUT YOUR IMAGE NAME)
my_image = "thumbs_up.jpg"
## END CODE HERE ##
# We preprocess your image to fit your algorithm.
fname = "images/" + my_image
image = np.array(ndimage.imread(fname, flatten=False))
my_image = scipy.misc.imresize(image, size=(64, 64)).reshape((1, 64 * 64 * 3)).T
my_image_prediction = predict(my_image, parameters)
plt.imshow(image)
print("Your algorithm predicts: y = " + str(np.squeeze(my_image_prediction)))
plt.show()
def GestureRecognition():
learning_rate = 0.0001
num_epochs = 1500
minibatch_size = 32
print_cost = True
is_plot = True
costs = []
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = tf_utils.load_dataset(
)
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0],
-1).T #每一列就是一个样本
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
m = X_train_flatten.shape[1]
#归一化数据
X_train = X_train_flatten / 255
X_test = X_test_flatten / 255
#转换为独热矩阵
Y_train = tf_utils.convert_to_one_hot(Y_train_orig, 6)
Y_test = tf_utils.convert_to_one_hot(Y_test_orig, 6)
tf.set_random_seed(1)
seed = 3
tf.compat.v1.reset_default_graph() # 用于清除默认图形堆栈并重置全局默认图形。
X = tf.compat.v1.placeholder(tf.float32, [X_train.shape[0], None],
name="X")
Y = tf.compat.v1.placeholder(tf.float32, [Y_train.shape[0], None],
name="Y")
parameters = InitParameter([12288, 25, 12, 6])
z = ForwardPropagation(X, parameters)
cost = ComputeCost(z, Y)
optimizer = tf.train.AdamOptimizer(
learning_rate=learning_rate).minimize(cost)
init = tf.global_variables_initializer()
with tf.compat.v1.Session() as sess:
sess.run(init)
for epoch in range(num_epochs):
epoch_cost = 0 # 每代的成本
num_minibatches = int(m / minibatch_size) # minibatch的总数量
seed = seed + 1
minibatches = tf_utils.random_mini_batches(X_train, Y_train,
minibatch_size, seed)
for minibatch in minibatches:
# 选择一个minibatch
(minibatch_X, minibatch_Y) = minibatch
# 数据已经准备好了，开始运行session
_, minibatch_cost = sess.run([optimizer, cost],
feed_dict={
X: minibatch_X,
Y: minibatch_Y
})
# 计算这个minibatch在这一代中所占的误差
epoch_cost = epoch_cost + minibatch_cost / num_minibatches
# 记录并打印成本
## 记录成本
if epoch % 5 == 0:
costs.append(epoch_cost)
# 是否打印：
if print_cost and epoch % 100 == 0:
print("epoch = " + str(epoch) + " epoch_cost = " +
str(epoch_cost))
# 是否绘制图谱
if is_plot:
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('iterations (per tens)')
plt.title("Learning rate =" + str(learning_rate))
plt.show()
# 保存学习后的参数
parameters = sess.run(parameters)
print("参数已经保存到session。")
# 计算当前的预测结果
correct_prediction = tf.equal(tf.argmax(z), tf.argmax(Y))
# 计算准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("训练集的准确率：", accuracy.eval({X: X_train, Y: Y_train}))
print("测试集的准确率:", accuracy.eval({X: X_test, Y: Y_test}))
return parameters
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
import time
np.random.seed(1)
# Loading the dataset
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, \
classes = load_dataset()
# Example of a picture
index = 0
print("y = " + str(np.squeeze(Y_train_orig[:, index])))
# Flatten the training and test images
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
# Normalize image vectors
X_train = X_train_flatten / 255.
X_test = X_test_flatten / 255.
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6)
Y_test = convert_to_one_hot(Y_test_orig, 6)
def create_placeholders(n_x, n_y):
# Author:yu
#使用tensorFlow构建神经网络
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
import tf_utils
import time
X_train_orig,Y_train_orig,X_test_orig,Y_test_orig,classes = tf_utils.load_dataset()
# index=11
# plt.imshow(X_train_orig[index])
# plt.show()
# print(np.squeeze(Y_train_orig))#从数组的形状中删除单维度条目，即把shape中为1的维度去掉
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0],-1).T#每一列就是一个样本
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0],-1).T
#归一化数据
X_train = X_train_flatten/255
X_test = X_test_flatten/255
#转换为独热矩阵
Y_train = tf_utils.convert_to_one_hot(Y_train_orig,6)
Y_test = tf_utils.convert_to_one_hot(Y_test_orig,6)
# print('训练集样本数 = '+str(X_train.shape[1]))
# print('测试集样本数 = '+str(X_test.shape[1]))
# print('X_train.shape:'+str(X_train.shape))
# print('Y_train.shape:'+str(Y_train.shape))
# print('X_test.shape:'+str(X_test.shape))
# %%
sess = tf.Session()
one = tf.ones((3, 3))
zero = tf.zeros((3, 3))
y = np.array([[1, 2, 3, 0, 3, 4, 2, 1, 1]])
c = 5
one_hot_matrix = tf.one_hot(indices=np.squeeze(y), depth=c, axis=0)
# 参数可以是tensor对象, 可以是np.ndarray/int...python对象
# 返回对象会增加一个维度, 所以或者压缩y的维度, 或者reshape
print(f'one\n{sess.run(one)}')
print(f'one\n{sess.run(zero)}')
print(f'one_hot_matrix\n{sess.run(one_hot_matrix)}')
# %%
X_train, Y_train, X_test, Y_test, classes = tf_utils.load_dataset()
# %%
print(f'X_train.shape = {X_train.shape}')
print(f'Y_train.shape = {Y_train.shape}')
print(f'X_test.shape = {X_test.shape}')
print(f'Y_test.shape = {Y_test.shape}')
print(f'classes.shape = {classes.shape}')
print(f'classes = {classes}')
# %%
print(f'Y = {Y_train[0,11]}')
plt.imshow(X_train[11])
# %%
C = 6
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
from tensorflow.python.framework import ops
from sys import exit
from sklearn import preprocessing
import mpl_finance as mplf
# Loading the dataset
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig = load_dataset()
'''
# Example of a K chart
index = 0
print("y = " + str(np.squeeze(Y_train_orig[:, index])))
print(X_train_orig[index].shape)
print(X_train_orig[index][:, :4].shape)
quotes = np.column_stack((range(20), X_train_orig[index][:, :4]))
quotes[:, [2, 3, 4]] = quotes[:, [3, 4, 2]]
fig, ax = plt.subplots()
mplf.candlestick_ohlc(ax, quotes, width=0.4, colorup='r', colordown='g')
plt.grid(False)
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=30)
plt.show()
'''
# Flatten the training and test images
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1)
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1)
# Normalize image vectors
print(X_train_flatten.shape)
import math import numpy as np import h5py import matplotlib.pyplot as plt import tensorflow as tf from tensorflow.python.framework import ops from sklearn import preprocessing from sklearn.preprocessing import OneHotEncoder from tf_utils import load_dataset, convert_to_one_hot from backwardPropagation import model from keras.utils import to_categorical X_train, X_test, y_train, y_test = load_dataset() # Take transpose of the input data and also normalize it X_train = X_train.T X_train = (X_train - X_train.mean()) / (X_train.max() - X_train.min()) X_train = X_train.fillna(0) X_test = X_test.T X_test = (X_test - X_test.mean()) / (X_test.max() - X_test.min()) X_test = X_test.fillna(0) # Convert training and test labels to one hot matrices y_train = to_categorical(y_train, 9) y_train = y_train.T #print(y_train)
