问深度学习的主要框架有哪些可以通过腾讯云服务器学习？

Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', ...)

UpSampling2D(size=(2, 2))

def generator(samples, batch_size=32):
    num_samples = len(samples)
    while 1:
        sklearn.utils.shuffle(samples)
        for offset in range(0, num_samples, batch_size):
            batch_samples = samples.iloc[offset:offset+batch_size]

            images = []
            angles = []
            for idx in range(batch_samples.shape[0]):
                name = './data/'+batch_samples.iloc[idx]['center']
                center_image = cv2.cvtColor( cv2.imread(name), cv2.COLOR_BGR2RGB )
                center_angle = float(batch_samples.iloc[idx]['dir'])
                images.append(center_image)
                angles.append(center_angle)

            # trim image to only see section with road
            X_train = np.array(images)
            y_train = np.array(angles)
            yield sklearn.utils.shuffle(X_train, y_train)

next(generator)

datagen = ImageDataGenerator(
            featurewise_center=False,
            samplewise_center=False,
            featurewise_std_normalization=False,
            samplewise_std_normalization=False,
            zca_whitening=False,
            width_shift_range=0.1,
            height_shift_range=0.1,
            horizontal_flip=True,
            vertical_flip=False)

# compute quantities required for featurewise normalization
datagen.fit(X_train)

def sgd_update(trainables, learning_rate=1e-2):
    for t in trainables:
        t.value = t.value - learning_rate * t.gradients[t]

#训练神经网络的过程
for i in range(epochs):
    loss = 0
    for j in range(steps_per_epoch):
        # 输入模块，将全部所有数据按照样本拆分成若干批次
        X_batch, y_batch = resample(X_, y_, n_samples=batch_size)
        # 各种深度学习零件搭建的深度神经网络
        forward_and_backward(graph)
        # 凸优化模块
        sgd_update(trainables, 0.1)

from keras.optimizers import Adam
adam = Adam()
model.compile(loss='categorical_crossentropy',
                optimizer=adam,
                metrics=['accuracy'])

# 初始化
from __future__ import print_function
import numpy as np
from keras.callbacks import TensorBoard
from keras.models import Sequential
from keras.optimizers import Adam
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPool2D
from keras.utils import np_utils
from keras import backend as K
from keras.callbacks import ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from keras.datasets import cifar10

from keras.backend.tensorflow_backend import set_session
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
set_session(tf.Session(config=config))


np.random.seed(0)
print("Initialized!")

# 定义变量
batch_size = 32
nb_classes = 10
nb_epoch = 50
img_rows, img_cols = 32, 32
nb_filters = [32, 32, 64, 64]
pool_size = (2, 2)
kernel_size = (3, 3)

#
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = X_train.astype("float32") / 255
X_test  = X_test.astype("float32") / 255

y_train = y_train
y_test = y_test


input_shape = (img_rows, img_cols, 3)
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

datagen = ImageDataGenerator(
            featurewise_center=False,
            samplewise_center=False,
            featurewise_std_normalization=False,
            samplewise_std_normalization=False,
            zca_whitening=False,
            rotation_range=0,
            width_shift_range=0.1,
            height_shift_range=0.1,
            horizontal_flip=True,
            vertical_flip=False)

datagen.fit(X_train)

model = Sequential()
model.add(Conv2D(nb_filters[0], kernel_size, padding='same',input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(nb_filters[1], kernel_size))
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=pool_size))
model.add(Dropout(0.25))

model.add(Conv2D(nb_filters[2], kernel_size, padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(nb_filters[3], kernel_size))
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=pool_size))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))

adam = Adam(lr=0.0001)
model.compile(loss='categorical_crossentropy',
                   optimizer=adam,
                   metrics=['accuracy'])

#训练模型
best_model = ModelCheckpoint("cifar10_best.h5", monitor='val_loss', verbose=0, save_best_only=True)
tb = TensorBoard(log_dir="./logs")
model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size),
                        steps_per_epoch=X_train.shape[0] // batch_size,
                        epochs=nb_epoch, verbose=1,
                        validation_data=(X_test, Y_test), callbacks=[best_model,tb])


# 模型评分
score = model.evaluate(X_test, Y_test, verbose=0)
# 输出结果
print('Test score:', score[0])
print("Accuracy: %.2f%%" % (score[1]*100))                   
print("Compiled!")

# python
import numpy as np
w1_0 = np.array([[ 0.1,  0.2,  0.3,  0.4],
                 [ 0.5,  0.6,  0.7,  0.8],
                 [ 0.9,  1.0,  1.1,  1.2]])
w2_0 = np.array([[ 1.3,  1.4],
                 [ 1.5,  1.6],
                 [ 1.7,  1.8],
                 [ 1.9,  2.0]])

b1_0 = np.array( [-2.0, -6.0, -1.0, -7.0])
b2_0 = np.array( [-2.5, -5.0])

# python

x = [1,2,3]
y = [-0.85, 0.72]

o1 = np.dot(x, w1_0 ) + b1_0
os1 = np.power(1+np.exp(o1*-1), -1)
o2 = np.dot(os1, w2_0) + b2_0
os2 = np.tanh(o2)

# python

alpha = 0.1
grad_os2 = (y - os2) * (1-np.power(os2, 2))
grad_os1 = np.dot(w2_0, grad_os2.T).T * (1-os1)*os1
grad_w2 = ...
grad_b2 = ...
...
...
w2_0    = w2_0 + alpha * grad_w2
b2_0    = b2_0 + alpha * grad_b2
...
...

# lua
function xxx:updateOutput(input)
   input.THNN.xxx_updateOutput(
      input:cdata(),
      self.output:cdata()
   )
   return self.output
end

function xxx:updateGradInput(input, gradOutput)
   input.THNN.xxx_updateGradInput(
      input:cdata(),
      gradOutput:cdata(),
      self.gradInput:cdata(),
      self.output:cdata()
   )
   return self.gradInput
end

# lua
void THNN_(Sigmoid_updateOutput)( THNNState
  *state, THTensor
  *input, THTensor
  *output)
{
  THTensor_(resizeAs)(output, input);

  TH_TENSOR_APPLY2(real, output, real, input,
    *output_data = 1./(1.+ exp(- *input_data));
  );
}

// c
void THNN_(Sigmoid_updateGradInput)(
          THNNState *state,
          THTensor *input,
          THTensor *gradOutput,
          THTensor *gradInput,
          THTensor *output)
{
  THNN_CHECK_NELEMENT(input, gradOutput);
  THTensor_(resizeAs)(gradInput, output);
  TH_TENSOR_APPLY3(real, gradInput, real, gradOutput, real, output,
    real z = * output_data;
    *gradInput_data = *gradOutput_data * (1. - z) * z;
  );
}

# python
class Node(object):
    """
    Base class for nodes in the network.

    Arguments:

        `inbound_nodes`: A list of nodes with edges into this node.
    """
    def __init__(self, inbound_nodes=[]):
        """
        Node's constructor (runs when the object is instantiated). Sets
        properties that all nodes need.
        """
        # A list of nodes with edges into this node.
        self.inbound_nodes = inbound_nodes
        # The eventual value of this node. Set by running
        # the forward() method.
        self.value = None
        # A list of nodes that this node outputs to.
        self.outbound_nodes = []
        # New property! Keys are the inputs to this node and
        # their values are the partials of this node with
        # respect to that input.
        self.gradients = {}

        # Sets this node as an outbound node for all of
        # this node's inputs.
        for node in inbound_nodes:
            node.outbound_nodes.append(self)

    def forward(self):
        """
        Every node that uses this class as a base class will
        need to define its own `forward` method.
        """
        raise NotImplementedError

    def backward(self):
        """
        Every node that uses this class as a base class will
        need to define its own `backward` method.
        """
        raise NotImplementedError


class Input(Node):
    """
    A generic input into the network.
    """
    def __init__(self):
        Node.__init__(self)

    def forward(self):
        pass

    def backward(self):
        self.gradients = {self: 0}
        for n in self.outbound_nodes:
            self.gradients[self] += n.gradients[self]

class Linear(Node):
    """
    Represents a node that performs a linear transform.
    """
    def __init__(self, X, W, b):
        Node.__init__(self, [X, W, b])

    def forward(self):
        """
        Performs the math behind a linear transform.
        """
        X = self.inbound_nodes[0].value
        W = self.inbound_nodes[1].value
        b = self.inbound_nodes[2].value
        self.value = np.dot(X, W) + b

    def backward(self):
        """
        Calculates the gradient based on the output values.
        """
        self.gradients = {n: np.zeros_like(n.value) for n in self.inbound_nodes}
        for n in self.outbound_nodes:
            grad_cost = n.gradients[self]
            self.gradients[self.inbound_nodes[0]] += np.dot(grad_cost, self.inbound_nodes[1].value.T)
            self.gradients[self.inbound_nodes[1]] += np.dot(self.inbound_nodes[0].value.T, grad_cost)
            self.gradients[self.inbound_nodes[2]] += np.sum(grad_cost, axis=0, keepdims=False)


class Sigmoid(Node):
    """
    Represents a node that performs the sigmoid activation function.
    """
    def __init__(self, node):
        Node.__init__(self, [node])

    def _sigmoid(self, x):
        """
        This method is separate from `forward` because it
        will be used with `backward` as well.

        `x`: A numpy array-like object.
        """
        return 1. / (1. + np.exp(-x))

    def forward(self):
        """
        Perform the sigmoid function and set the value.
        """
        input_value = self.inbound_nodes[0].value
        self.value = self._sigmoid(input_value)

    def backward(self):
        """
        Calculates the gradient using the derivative of
        the sigmoid function.
        """
        self.gradients = {n: np.zeros_like(n.value) for n in self.inbound_nodes}
        for n in self.outbound_nodes:
            grad_cost = n.gradients[self]
            sigmoid = self.value
            self.gradients[self.inbound_nodes[0]] += sigmoid * (1 - sigmoid) * grad_cost

class Tanh(Node):
    def __init__(self, node):
        """
        The tanh cost function.
        Should be used as the last node for a network.
        """
        Node.__init__(self, [node])

    def forward(self):
        """
        Calculates the tanh.
        """
        input_value = self.inbound_nodes[0].value
        self.value  = np.tanh(input_value)

    def backward(self):
        """
        Calculates the gradient of the cost.
        """
        self.gradients = {n: np.zeros_like(n.value) for n in self.inbound_nodes}
        for n in self.outbound_nodes:
            grad_cost = n.gradients[self]
            tanh = self.value
            self.gradients[self.inbound_nodes[0]] += (1 + tanh) * (1 - tanh) * grad_cost.T



class MSE(Node):
    def __init__(self, y, a):
        """
        The mean squared error cost function.
        Should be used as the last node for a network.
        """
        Node.__init__(self, [y, a])

    def forward(self):
        """
        Calculates the mean squared error.
        """
        y = self.inbound_nodes[0].value.reshape(-1, 1)
        a = self.inbound_nodes[1].value.reshape(-1, 1)

        self.m = self.inbound_nodes[0].value.shape[0]
        self.diff = y - a
        self.value = np.mean(self.diff**2)

    def backward(self):
        """
        Calculates the gradient of the cost.
        """
        self.gradients[self.inbound_nodes[0]] = (2 / self.m) * self.diff
        self.gradients[self.inbound_nodes[1]] = (-2 / self.m) * self.diff

# python
def topological_sort(feed_dict):
    """
    Sort the nodes in topological order using Kahn's Algorithm.

    `feed_dict`: A dictionary where the key is a `Input` Node and the value is the respective value feed to that Node.

    Returns a list of sorted nodes.
    """
    input_nodes = [n for n in feed_dict.keys()]
    G = {}
    nodes = [n for n in input_nodes]
    while len(nodes) > 0:
        n = nodes.pop(0)
        if n not in G:
            G[n] = {'in': set(), 'out': set()}
        for m in n.outbound_nodes:
            if m not in G:
                G[m] = {'in': set(), 'out': set()}
            G[n]['out'].add(m)
            G[m]['in'].add(n)
            nodes.append(m)

    L = []
    S = set(input_nodes)
    while len(S) > 0:
        n = S.pop()
        if isinstance(n, Input):
            n.value = feed_dict[n]

        L.append(n)
        for m in n.outbound_nodes:
            G[n]['out'].remove(m)
            G[m]['in'].remove(n)
            if len(G[m]['in']) == 0:
                S.add(m)
    return L


def forward_and_backward(graph):
    """
    Performs a forward pass and a backward pass through a list of sorted Nodes.

    Arguments:

        `graph`: The result of calling `topological_sort`.
    """
    for n in graph:
        n.forward()

    for n in graph[::-1]:
        n.backward()


def sgd_update(trainables, learning_rate=1e-2):
    """
    Updates the value of each trainable with SGD.

    Arguments:

        `trainables`: A list of `Input` Nodes representing weights/biases.
        `learning_rate`: The learning rate.
    """
    for t in trainables:
        t.value = t.value - learning_rate * t.gradients[t]

# python

import numpy as np
from sklearn.utils import resample
np.random.seed(0)

w1_0 = np.array([[ 0.1,  0.2,  0.3,  0.4],
                 [ 0.5,  0.6,  0.7,  0.8],
                 [ 0.9,  1.0,  1.1,  1.2]])
w2_0 = np.array([[ 1.3,  1.4],
                 [ 1.5,  1.6],
                 [ 1.7,  1.8],
                 [ 1.9,  2.0]])
b1_0 = np.array( [-2.0, -6.0, -1.0, -7.0])
b2_0 = np.array( [-2.5, -5.0])

X_ = np.array([[1.0, 2.0, 3.0]])
y_ = np.array([[-0.85, 0.75]])
n_features = X_.shape[1]

W1_ = w1_0
b1_ = b1_0
W2_ = w2_0
b2_ = b2_0

X, y = Input(), Input()
W1, b1 = Input(), Input()
W2, b2 = Input(), Input()

l1 = Linear(X, W1, b1)
s1 = Sigmoid(l1)
l2 = Linear(s1, W2, b2)
t1 = Tanh(l2)
cost = MSE(y, t1)

feed_dict = {
    X: X_,   y: y_,
    W1: W1_, b1: b1_,
    W2: W2_, b2: b2_
}

epochs = 10
m = X_.shape[0]
batch_size = 1
steps_per_epoch = m // batch_size

graph = topological_sort(feed_dict)
trainables = [W1, b1, W2, b2]

l_Mat_W1 = [w1_0]
l_Mat_W2 = [w2_0]
l_Mat_out = []

l_val = []
for i in range(epochs):
    loss = 0
    for j in range(steps_per_epoch):
        X_batch, y_batch = resample(X_, y_, n_samples=batch_size)
        X.value = X_batch
        y.value = y_batch
        forward_and_backward(graph)
        sgd_update(trainables, 0.1)
        loss += graph[-1].value

    mat_W1 = []
    mat_W2 = []
    for i in graph:
        try:
            if  (i.value.shape[0] == 3) and (i.value.shape[1] == 4):
                mat_W1 = i.value
            if  (i.value.shape[0] == 4) and (i.value.shape[1] == 2):
                mat_W2 = i.value
        except:
            pass

    l_Mat_W1.append(mat_W1)
    l_Mat_W2.append(mat_W2)
    l_Mat_out.append(graph[9].value)

# python
import matplotlib.pyplot as plt
%matplotlib inline

fig = plt.figure( figsize=(14,10))
ax0 = fig.add_subplot(131)
#aax0 = fig.add_axes([0, 0, 0.3, 0.1])
c0 = ax0.imshow(np.array(l_Mat_out).reshape([-1,2]).T, interpolation='nearest',aspect='auto', cmap="Reds", vmax=1, vmin=-1)
ax0.set_title("Output")

cbar = fig.colorbar(c0, ticks=[-1, 0, 1])



ax1 = fig.add_subplot(132)
c1 = ax1.imshow(np.array(l_Mat_W1).reshape(len(l_Mat_W1), 12).T, interpolation='nearest',aspect='auto', cmap="Reds")
ax1.set_title("w1")
cbar = fig.colorbar(c1, ticks=[np.min(np.array(l_Mat_W1)), np.max(np.array(l_Mat_W1))])

ax2 = fig.add_subplot(133)
c2 = ax2.imshow(np.array(l_Mat_W2).reshape(len(l_Mat_W2), 8).T, interpolation='nearest',aspect='auto', cmap="Reds")
ax2.set_title("w2")
cbar = fig.colorbar(c2, ticks=[np.min(np.array(l_Mat_W2)), np.max(np.array(l_Mat_W2))])

ax0.set_yticks([0,1])
ax0.set_yticklabels(["out0", "out1"])

ax1.set_xlabel("epochs")
#for i in range(len(l_Mat_W1)):