1张量
张量可以使用GPU加速，可以自动将python内置数据类型转换为张量。张量有形状和数据类型。张量与numpy主要区别为：1张量可以用GPU加速2张量不可变。
Tensors和Numpy ndarrays可以自动相互转换。Tensors使用.numpy()方法可以显示转换为ndarray。这种转换让Tensors和ndarray共享了底层内存。Tensors可能在GPU内存可是Numpy总是在cpu内存，转换涉及从GPU到主机内存的复制。
import numpy as np
ndarray = np.ones([3, 3])
print("TensorFlow operations convert numpy arrays to Tensors automatically")
tensor = tf.multiply(ndarray, 42)
print(tensor)
print("And NumPy operations convert Tensors to numpy arrays automatically")
print(np.add(tensor, 1))
print("The .numpy() method explicitly converts a Tensor to a numpy array")
print(tensor.numpy())
TensorFlow operations convert numpy arrays to Tensors automatically tf.Tensor( [[42. 42. 42.] [42. 42. 42.] [42. 42. 42.]], shape=(3, 3), dtype=float64) And NumPy operations convert Tensors to numpy arrays automatically [[43. 43. 43.] [43. 43. 43.] [43. 43. 43.]] The .numpy() method explicitly converts a Tensor to a numpy array [[42. 42. 42.] [42. 42. 42.] [42. 42. 42.]]
Tensorflow自动决定是否使用GPU来加速运算。Tensor.device提供了设备信息。可以显示设置执行计算的设备。
Dateset：
tf.data.Dataset将数据喂给算法，迭代简单。Dataset.from_tensors,Dataset.from_tensor_slices,TextLineDataset,TFRecordDataset可以创建dataset。map,batch,shuffle用来对dataset转换处理。dataset可以直接用于迭代（如for循环）。
ds_tensors = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6]) # Create a CSV file import tempfile _, filename = tempfile.mkstemp() with open(filename, 'w') as f: f.write("""Line 1 Line 2 Line 3 """) ds_file = tf.data.TextLineDataset(filename) ds_tensors = ds_tensors.map(tf.square).shuffle(2).batch(2) ds_file = ds_file.batch(2) print('Elements of ds_tensors:') for x in ds_tensors: print(x) print(' Elements in ds_file:') for x in ds_file: print(x) Elements of ds_tensors: tf.Tensor([4 1], shape=(2,), dtype=int32) tf.Tensor([ 9 16], shape=(2,), dtype=int32) tf.Tensor([25 36], shape=(2,), dtype=int32) Elements in ds_file: tf.Tensor([b'Line 1' b'Line 2'], shape=(2,), dtype=string) tf.Tensor([b'Line 3' b' '], shape=(2,), dtype=string)
2自动微分
利用张量类型在描述清楚变量之间关系，TensorFlow自动计算变量的微分和梯度。tf.GradientTape用来计算自动微分。
x = tf.ones((2, 2)) with tf.GradientTape() as t: t.watch(x) y = tf.reduce_sum(x) z = tf.multiply(y, y) # Derivative of z with respect to the original input tensor x dz_dx = t.gradient(z, x) for i in [0, 1]: for j in [0, 1]: assert dz_dx[i][j].numpy() == 8.0
也可对中间变量进行微分：
x = tf.ones((2, 2)) with tf.GradientTape() as t: t.watch(x) y = tf.reduce_sum(x) z = tf.multiply(y, y) # Use the tape to compute the derivative of z with respect to the # intermediate value y. dz_dy = t.gradient(z, y) assert dz_dy.numpy() == 8.0
默认只要调用gradient方法，GradientTape持有的资源就会被释放。为了对多个变量进行微分，需要进行tf.GradientTape(persistent=True)设置。如：
x = tf.constant(3.0) with tf.GradientTape(persistent=True) as t: t.watch(x) y = x * x z = y * y dz_dx = t.gradient(z, x) # 108.0 (4*x^3 at x = 3) dy_dx = t.gradient(y, x) # 6.0 del t # Drop the reference to the tape
可以对导数再进行求导（二阶导数）：
x = tf.Variable(1.0) # Create a Tensorflow variable initialized to 1.0 with tf.GradientTape() as t: with tf.GradientTape() as t2: y = x * x * x # Compute the gradient inside the 't' context manager # which means the gradient computation is differentiable as well. dy_dx = t2.gradient(y, x) d2y_dx2 = t.gradient(dy_dx, x) assert dy_dx.numpy() == 3.0 assert d2y_dx2.numpy() == 6.0
3自定义训练
线性拟合实例。
class Model(object): def __init__(self): # Initialize variable to (5.0, 0.0) # In practice, these should be initialized to random values. self.W = tf.Variable(5.0) self.b = tf.Variable(0.0) def __call__(self, x): return self.W * x + self.b model = Model() assert model(3.0).numpy() == 15.0 def loss(predicted_y, desired_y): #定义损失函数 return tf.reduce_mean(tf.square(predicted_y - desired_y)) TRUE_W = 3.0 TRUE_b = 2.0 NUM_EXAMPLES = 1000 inputs = tf.random_normal(shape=[NUM_EXAMPLES]) noise = tf.random_normal(shape=[NUM_EXAMPLES]) outputs = inputs * TRUE_W + TRUE_b + noise #有噪声的y值 import matplotlib.pyplot as plt #画出预测值和真实值图 plt.scatter(inputs, outputs, c='b') plt.scatter(inputs, model(inputs), c='r') plt.show() print('Current loss: '), print(loss(model(inputs), outputs).numpy())
def train(model, inputs, outputs, learning_rate): #更新权重
with tf.GradientTape() as t:
current_loss = loss(model(inputs), outputs)
dW, db = t.gradient(current_loss, [model.W, model.b])
model.W.assign_sub(learning_rate * dW)
model.b.assign_sub(learning_rate * db)
model = Model()
# Collect the history of W-values and b-values to plot later
Ws, bs = [], []
epochs = range(10)
for epoch in epochs: #迭代10次
Ws.append(model.W.numpy())
bs.append(model.b.numpy())
current_loss = loss(model(inputs), outputs)
train(model, inputs, outputs, learning_rate=0.1)
print('Epoch %2d: W=%1.2f b=%1.2f, loss=%2.5f' %
(epoch, Ws[-1], bs[-1], current_loss))
# Let's plot it all
plt.plot(epochs, Ws, 'r',
epochs, bs, 'b')
plt.plot([TRUE_W] * len(epochs), 'r--',
[TRUE_b] * len(epochs), 'b--')
plt.legend(['W', 'b', 'true W', 'true_b'])
plt.show()
#随着迭代次数增加，loss越来越少Epoch 0: W=5.00 b=0.00, loss=9.36370 Epoch 1: W=4.59 b=0.42, loss=6.28548 Epoch 2: W=4.26 b=0.75, loss=4.34777 Epoch 3: W=4.00 b=1.01, loss=3.12800 Epoch 4: W=3.80 b=1.22, loss=2.36017 Epoch 5: W=3.64 b=1.39, loss=1.87683 Epoch 6: W=3.51 b=1.52, loss=1.57257 Epoch 7: W=3.40 b=1.62, loss=1.38104 Epoch 8: W=3.32 b=1.70, loss=1.26047 Epoch 9: W=3.26 b=1.77, loss=1.18457
4keras
Keras是一种高级抽象的API。
# In the tf.keras.layers package, layers are objects. To construct a layer, # simply construct the object. Most layers take as a first argument the number # of output dimensions / channels. layer = tf.keras.layers.Dense(100) # The number of input dimensions is often unnecessary, as it can be inferred # the first time the layer is used, but it can be provided if you want to 输入可选 # specify it manually, which is useful in some complex models. layer = tf.keras.layers.Dense(10, input_shape=(None, 5))
层的类型包括Dense（全连接层），Conv2D，LSTM，BatchNormalization，Dropout。
# To use a layer, simply call it. layer(tf.zeros([10, 5])) #提供输入，开始计算 <tf.Tensor: id=29, shape=(10, 10), dtype=float32, numpy= array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)> # Layers have many useful methods. For example, you can inspect all variables # in a layer using `layer.variables` and trainable variables using # `layer.trainable_variables`. In this case a fully-connected layer # will have variables for weights and biases. layer.variables #权重包括w和b [<tf.Variable 'dense_1/kernel:0' shape=(5, 10) dtype=float32, numpy= array([[ 0.24547583, -0.18174013, -0.517627 , -0.28625917, 0.46390074, -0.11792481, 0.25204027, -0.10177726, -0.6321482 , 0.11025655], [-0.04801774, 0.05520332, -0.61134326, -0.380753 , -0.20825565, 0.44676226, 0.34310788, -0.18177858, -0.0399543 , -0.6257955 ], [-0.09570092, 0.2136836 , 0.11427677, 0.45249623, 0.02414119, 0.2739644 , -0.5701976 , -0.28433737, 0.1094352 , -0.26321137], [ 0.6225632 , 0.56247157, 0.14319342, -0.27484533, 0.06545639, -0.14055312, 0.02605182, -0.17947513, -0.43184835, -0.13298517], [ 0.21471226, -0.34008306, 0.13555825, -0.20253879, -0.14976257, 0.24820238, 0.4052704 , -0.42966282, 0.46730322, 0.5801386 ]], dtype=float32)>, <tf.Variable 'dense_1/bias:0' shape=(10,) dtype=float32, numpy=array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)>]
class MyDenseLayer(tf.keras.layers.Layer): #封装
def __init__(self, num_outputs):
super(MyDenseLayer, self).__init__() #调用基类构造函数
self.num_outputs = num_outputs
def build(self, input_shape):
self.kernel = self.add_variable("kernel",
shape=[int(input_shape[-1]),
self.num_outputs])
def call(self, input):
return tf.matmul(input, self.kernel)
layer = MyDenseLayer(10)
print(layer(tf.zeros([10, 5])))
print(layer.trainable_variables)
tf.Tensor(
[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]], shape=(10, 10), dtype=float32)
[<tf.Variable 'my_dense_layer/kernel:0' shape=(5, 10) dtype=float32, numpy=
array([[ 0.40808827, 0.31189376, -0.6253119 , -0.5369232 , -0.6137004 ,
-0.5453413 , -0.27606115, -0.60044795, 0.42764622, 0.25756943],
[ 0.11746502, 0.40135378, 0.27446055, 0.12815869, 0.19679207,
0.118303 , -0.5602601 , -0.3910997 , -0.50229526, -0.4392987 ],
[-0.11431473, 0.30567902, -0.42285785, 0.41714746, 0.54528743,
0.20401049, -0.0829075 , 0.4709614 , -0.60372585, -0.45935804],
[-0.51994437, -0.40799683, 0.306705 , 0.588075 , 0.12873381,
-0.12829626, 0.03449196, 0.5080891 , -0.5090939 , 0.1574735 ],
[-0.30528757, -0.3296884 , 0.168805 , 0.40543085, 0.46509403,
-0.52575713, -0.181254 , -0.24681184, -0.37497327, -0.37618726]],
dtype=float32)>]
将tf.keras.Model 封装，自定义类
class ResnetIdentityBlock(tf.keras.Model): def __init__(self, kernel_size, filters): super(ResnetIdentityBlock, self).__init__(name='') filters1, filters2, filters3 = filters self.conv2a = tf.keras.layers.Conv2D(filters1, (1, 1)) self.bn2a = tf.keras.layers.BatchNormalization() self.conv2b = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same') self.bn2b = tf.keras.layers.BatchNormalization() self.conv2c = tf.keras.layers.Conv2D(filters3, (1, 1)) self.bn2c = tf.keras.layers.BatchNormalization() def call(self, input_tensor, training=False): #重载调用运算符 x = self.conv2a(input_tensor) x = self.bn2a(x, training=training) x = tf.nn.relu(x) x = self.conv2b(x) x = self.bn2b(x, training=training) x = tf.nn.relu(x) x = self.conv2c(x) x = self.bn2c(x, training=training) x += input_tensor return tf.nn.relu(x) block = ResnetIdentityBlock(1, [1, 2, 3]) print(block(tf.zeros([1, 2, 3, 3]))) print([x.name for x in block.trainable_variables]) tf.Tensor( [[[[0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.]]]], shape=(1, 2, 3, 3), dtype=float32) ['resnet_identity_block/conv2d/kernel:0', 'resnet_identity_block/conv2d/bias:0', 'resnet_identity_block/batch_normalization/gamma:0', 'resnet_identity_block/batch_normalization/beta:0', 'resnet_identity_block/conv2d_1/kernel:0', 'resnet_identity_block/conv2d_1/bias:0', 'resnet_identity_block/batch_normalization_1/gamma:0', 'resnet_identity_block/batch_normalization_1/beta:0', 'resnet_identity_block/conv2d_2/kernel:0', 'resnet_identity_block/conv2d_2/bias:0', 'resnet_identity_block/batch_normalization_2/gamma:0', 'resnet_identity_block/batch_normalization_2/beta:0']
更常用的是tf.keras.Sequential函数，将每层逐一写出：
my_seq = tf.keras.Sequential([tf.keras.layers.Conv2D(1, (1, 1)), tf.keras.layers.BatchNormalization(), tf.keras.layers.Conv2D(2, 1, padding='same'), tf.keras.layers.BatchNormalization(), tf.keras.layers.Conv2D(3, (1, 1)), tf.keras.layers.BatchNormalization()]) my_seq(tf.zeros([1, 2, 3, 3])) <tf.Tensor: id=514, shape=(1, 2, 3, 3), dtype=float32, numpy= array([[[[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]], [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]]], dtype=float32)>
6按品种对鸢尾花进行分类