DeepLearning.ai深度学习课程笔记
  • Introduction
  • 第一门课 神经网络和深度学习(Neural-Networks-and-Deep-Learning)
    • 第一周:深度学习引言(Introduction to Deep Learning)
      • 1.1 神经网络的监督学习(Supervised Learning with Neural Networks)
      • 1.2 为什么神经网络会流行?(Why is Deep Learning taking off?)
    • 第二周:神经网络的编程基础(Basics of Neural Network programming)
      • 2.1 二分类(Binary Classification)
      • 2.2 逻辑回归(Logistic Regression)
      • 2.3 逻辑回归的代价函数(Logistic Regression Cost Function)
      • 2.4 逻辑回归的梯度下降(Logistic Regression Gradient Descent)
      • 2.5 梯度下降的例子(Gradient Descent on m Examples)
      • 2.6 向量化 logistic 回归的梯度输出(Vectorizing Logistic Regression’s Gradient Output)
      • 2.7 (选修)logistic 损失函数的解释(Explanation of logistic regression cost function )
      • Logistic Regression with a Neural Network mindset 代码
      • lr_utils.py
    • 第三周:浅层神经网络(Shallow neural networks)
      • 3.1 神经网络概述(Neural Network Overview)
      • 3.2 神经网络的表示(Neural Network Representation )
      • 3.3 计算一个神经网络的输出(Computing a Neural Network's output )
      • 3.4 多样本向量化(Vectorizing across multiple examples )
      • 3.5 激活函数(Activation functions)
      • 3.6 为什么需要( 非线性激活函数?(why need a nonlinear activation function?)
      • 3.7 激活函数的导数(Derivatives of activation functions )
      • 3.8 神经网络的梯度下降(Gradient descent for neural networks)
      • 3.9 (选修)直观理解反向传播(Backpropagation intuition )
      • 3.10 随机初始化(Random+Initialization)
      • Planar data classification with one hidden layer
      • planar_utils.py
      • testCases.py
    • 第四周:深层神经网络(Deep Neural Networks)
      • 4.1 深层神经网络(Deep L-layer neural network)
      • 4.2 前向传播和反向传播(Forward and backward propagation)
      • 4.3 深层网络中的前向传播(Forward propagation in a Deep Network )
      • 4.4 为什么使用深层表示?(Why deep representations?)
      • 4.5 搭建神经网络块(Building blocks of deep neural networks)
      • 4.6 参数 VS 超参数(Parameters vs Hyperparameters)
      • Building your Deep Neural Network Step by Step
      • dnn_utils.py
      • testCases.py
      • Deep Neural Network Application
      • dnn_app_utils.py
  • 第二门课 改善深层神经网络:超参数调试、 正 则 化 以 及 优 化 (Improving Deep Neural Networks:Hyperparameter tuning, Regulariza
    • 第二门课 改善深层神经网络:超参数调试、正则化以及优化(Improving Deep Neural Networks:Hyperparameter tuning, Regularization and
      • 第一周:深度学习的实用层面(Practical aspects of Deep Learning)
        • 1.1 训练,验证,测试集(Train / Dev / Test sets)
        • 1.2 偏差,方差(Bias /Variance)
        • 1.3 机器学习基础(Basic Recipe for Machine Learning)
        • 1.4 正则化(Regularization)
        • 1.5 为什么正则化有利于预防过拟合呢?(Why regularization reduces overfitting?)
        • 1.6 dropout 正则化(Dropout Regularization)
        • 1.7 理解 dropout(Understanding Dropout)
        • 1.8 其他正则化方法(Other regularization methods)
        • 1.9 归一化输入(Normalizing inputs)
        • 1.10 梯度消失/梯度爆炸(Vanishing / Exploding gradients)
        • 1.11 神经网络的权重初始化(Weight Initialization for Deep Networks)
        • 1.12 梯度的数值逼近(Numerical approximation of gradients)
        • 1.13 梯度检验(Gradient checking)
        • 1.14 梯度检验应用的注意事项(Gradient Checking Implementation Notes)
        • Initialization
        • Gradient Checking
        • Regularization
        • reg_utils.py
        • testCases.py
      • 第二周:优化算法 (Optimization algorithms)
        • 2.1 Mini-batch 梯度下降(Mini-batch gradient descent)
        • 2.2 理解 mini-batch 梯度下降法(Understanding mini-batch gradient descent)
        • 2.3 指数加权平均数(Exponentially weighted averages)
        • 2.4 理解指数加权平均数(Understanding exponentially weighted averages )
        • 2.5 指 数 加 权 平 均 的 偏 差 修 正 ( Bias correction in exponentially weighted averages )
        • 2.6 动量梯度下降法(Gradient descent with Momentum )
        • 2.7 RMSprop( root mean square prop)
        • 2.8 Adam 优化算法(Adam optimization algorithm)
        • 2.9 学习率衰减(Learning rate decay)
        • 2.10 局部最优的问题(The problem of local optima)
        • Optimization
        • opt_utils.py
        • testCases.py
      • 第 三 周 超 参 数 调 试 、 Batch 正 则 化 和 程 序 框 架 (Hyperparameter tuning)
        • 3.1 调试处理(Tuning process)
        • 3.2 为超参数选择合适的范围(Using an appropriate scale to pick hyperparameters)
        • 3.3 超参数训练的实践: Pandas VS Caviar(Hyperparameters tuning in practice: Pandas vs. Caviar)
        • 3.4 归一化网络的激活函数( Normalizing activations in a network)
        • 3.5 将 Batch Norm 拟合进神经网络(Fitting Batch Norm into a neural network)
        • 3.6 Batch Norm 为什么奏效?(Why does Batch Norm work?)
        • 3.7 测试时的 Batch Norm(Batch Norm at test time)
        • 3.8 Softmax 回归(Softmax regression)
        • 3.9 训练一个 Softmax 分类器(Training a Softmax classifier)
        • tensorflow tutorial
        • improv_utils.py
        • tf_utils.py
  • 第三门课 结构化机器学习项目(Structuring Machine Learning Projects)
    • 第三门课 结构化机器学习项目(Structuring Machine Learning Projects)
      • 第一周 机器学习(ML)策略(1)(ML strategy(1))
        • 1.1 为什么是 ML 策略?(Why ML Strategy?)
        • 1.2 正交化(Orthogonalization)
        • 1.3 单一数字评估指标(Single number evaluation metric)
        • 1.4 满足和优化指标(Satisficing and optimizing metrics)
        • 1.5 训练/开发/测试集划分(Train/dev/test distributions)
        • 1.6 开发集和测试集的大小(Size of dev and test sets)
        • 1.7 什么时候该改变开发/测试集和指标?(When to change dev/test sets and metrics)
        • 1.8 为什么是人的表现?( Why human-level performance?)
        • 1.9 可避免偏差(Avoidable bias)
        • 1.10 理解人的表现(Understanding human-level performance)
        • 1.11 超过人的表现(Surpassing human- level performance)
        • 1.12 改善你的模型的表现(Improving your model performance)
      • 第二周:机器学习策略(2)(ML Strategy (2))
        • 2.1 进行误差分析(Carrying out error analysis)
        • 2.2 清楚标注错误的数据(Cleaning up Incorrectly labeled data)
        • 2.3 快速搭建你的第一个系统,并进行迭代(Build your first system quickly, then iterate)
        • 2.4 在不同的划分上进行训练并测试(Training and testing on different distributions)
        • 2.5 不匹配数据划分的偏差和方差(Bias and Variance with mismatched data distributions)
        • 2.6 定位数据不匹配(Addressing data mismatch)
        • 2.7 迁移学习(Transfer learning)
        • 2.8 多任务学习(Multi-task learning)
        • 2.9 什么是端到端的深度学习?(What is end-to-end deep learning?)
        • 2.10 是否要使用端到端的深度学习?(Whether to use end-to-end learning?)
  • 第四门课 卷积神经网络(Convolutional Neural Networks)
    • 第四门课 卷积神经网络(Convolutional Neural Networks)
      • 第一周 卷积神经网络(Foundations of Convolutional Neural Networks)
        • 1.1 计算机视觉(Computer vision)
        • 1.2 边缘检测示例(Edge detection example)
        • 1.3 更多边缘检测内容(More edge detection)
        • 1.4 Padding
        • 1.5 卷积步长(Strided convolutions)
        • 1.6 三维卷积(Convolutions over volumes)
        • 1.7 单层卷积网络(One layer of a convolutional network)
        • 1.8 简单卷积网络示例(A simple convolution network example)
        • 1.9 池化层(Pooling layers)
        • 1.10 卷积神经网络示例(Convolutional neural network example)
        • 1.11 为什么使用卷积?(Why convolutions?)
        • Convolution model Step by Step
        • Convolutional Neural Networks: Application
        • cnn_utils
      • 第二周 深度卷积网络:实例探究(Deep convolutional models: case studies)
        • 2.1 经典网络(Classic networks)
        • 2.2 残差网络(Residual Networks (ResNets))
        • 2.3 残差网络为什么有用?(Why ResNets work?)
        • 2.4 网络中的网络以及 1×1 卷积(Network in Network and 1×1 convolutions)
        • 2.5 谷歌 Inception 网络简介(Inception network motivation)
        • 2.6 Inception 网络(Inception network)
        • 2.7 迁移学习(Transfer Learning)
        • 2.8 数据扩充(Data augmentation)
        • 2.9 计算机视觉现状(The state of computer vision)
        • Residual Networks
        • Keras tutorial - the Happy House
        • kt_utils.py
      • 第三周 目标检测(Object detection)
        • 3.1 目标定位(Object localization)
        • 3.2 特征点检测(Landmark detection)
        • 3.3 目标检测(Object detection)
        • 3.4 卷积的滑动窗口实现(Convolutional implementation of sliding windows)
        • 3.5 Bounding Box预测(Bounding box predictions)
        • 3.6 交并比(Intersection over union)
        • 3.7 非极大值抑制(Non-max suppression)
        • 3.8 Anchor Boxes
        • 3.9 YOLO 算法(Putting it together: YOLO algorithm)
        • 3.10 候选区域(选修)(Region proposals (Optional))
        • Autonomous driving application - Car detection
        • yolo_utils.py
      • 第四周 特殊应用:人脸识别和神经风格转换(Special applications: Face recognition &Neural style transfer)
        • 4.1 什么是人脸识别?(What is face recognition?)
        • 4.2 One-Shot学习(One-shot learning)
        • 4.3 Siamese 网络(Siamese network)
        • 4.4 Triplet 损失(Triplet 损失)
        • 4.5 面部验证与二分类(Face verification and binary classification)
        • 4.6 什么是深度卷积网络?(What are deep ConvNets learning?)
        • 4.7 代价函数(Cost function)
        • 4.8 内容代价函数(Content cost function)
        • 4.9 风格代价函数(Style cost function)
        • 4.10 一维到三维推广(1D and 3D generalizations of models)
        • Art Generation with Neural Style Transfer
        • nst_utils.py
        • Face Recognition for the Happy House
        • fr_utils.py
        • inception_blocks.py
  • 第五门课 序列模型(Sequence Models)
    • 第五门课 序列模型(Sequence Models)
      • 第一周 循环序列模型(Recurrent Neural Networks)
        • 1.1 为什么选择序列模型?(Why Sequence Models?)
        • 1.2 数学符号(Notation)
        • 1.3 循环神经网络模型(Recurrent Neural Network Model)
        • 1.4 通过时间的反向传播(Backpropagation through time)
        • 1.5 不同类型的循环神经网络(Different types of RNNs)
        • 1.6 语言模型和序列生成(Language model and sequence generation)
        • 1.7 对新序列采样(Sampling novel sequences)
        • 1.8 循环神经网络的梯度消失(Vanishing gradients with RNNs)
        • 1.9 GRU单元(Gated Recurrent Unit(GRU))
        • 1.10 长短期记忆(LSTM(long short term memory)unit)
        • 1.11 双向循环神经网络(Bidirectional RNN)
        • 1.12 深层循环神经网络(Deep RNNs)
        • Building your Recurrent Neural Network
        • rnn_utils.py
        • Dinosaurus Island -- Character level language model final
        • utils.py
        • shakespeare_utils.py
        • Improvise a Jazz Solo with an LSTM Network
      • 第二周 自然语言处理与词嵌入(Natural Language Processing and Word Embeddings)
        • 2.1 词汇表征(Word Representation)
        • 2.2 使用词嵌入(Using Word Embeddings)
        • 2.3 词嵌入的特性(Properties of Word Embeddings)
        • 2.4 嵌入矩阵(Embedding Matrix)
        • 2.5 学习词嵌入(Learning Word Embeddings)
        • 2.6 Word2Vec
        • 2.7 负采样(Negative Sampling)
        • 2.8 GloVe 词向量(GloVe Word Vectors)
        • 2.9 情感分类(Sentiment Classification)
        • 2.10 词嵌入除偏(Debiasing Word Embeddings)
        • Operations on word vectors
        • w2v_utils.py
        • Emojify
        • emo_utils.py
      • 第三周 序列模型和注意力机制(Sequence models & Attention mechanism)
        • 3.1 基础模型(Basic Models)
        • 3.2 选择最可能的句子(Picking the most likely sentence)
        • 3.3 集束搜索(Beam Search)
        • 3.4 改进集束搜索(Refinements to Beam Search)
        • 3.5 集束搜索的误差分析(Error analysis in beam search)
        • 3.6 Bleu 得分(选修)(Bleu Score (optional))
        • 3.7 注意力模型直观理解(Attention Model Intuition)
        • 3.8注意力模型(Attention Model)
        • 3.9语音识别(Speech recognition)
        • 3.10触发字检测(Trigger Word Detection)
        • Neural machine translation with attention
        • nmt_utils.py
        • Trigger word detection
        • td_utils.py
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  1. 第二门课 改善深层神经网络:超参数调试、 正 则 化 以 及 优 化 (Improving Deep Neural Networks:Hyperparameter tuning, Regulariza
  2. 第二门课 改善深层神经网络:超参数调试、正则化以及优化(Improving Deep Neural Networks:Hyperparameter tuning, Regularization and
  3. 第 三 周 超 参 数 调 试 、 Batch 正 则 化 和 程 序 框 架 (Hyperparameter tuning)

improv_utils.py

import h5py
import numpy as np
import tensorflow as tf
import math

def load_dataset():
    train_dataset = h5py.File('datasets/train_signs.h5', "r")
    train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
    train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels

    test_dataset = h5py.File('datasets/test_signs.h5', "r")
    test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
    test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels

    classes = np.array(test_dataset["list_classes"][:]) # the list of classes

    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))

    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes


def random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):
    """
    Creates a list of random minibatches from (X, Y)

    Arguments:
    X -- input data, of shape (input size, number of examples)
    Y -- true "label" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)
    mini_batch_size - size of the mini-batches, integer
    seed -- this is only for the purpose of grading, so that you're "random minibatches are the same as ours.

    Returns:
    mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y)
    """

    m = X.shape[1]                  # number of training examples
    mini_batches = []
    np.random.seed(seed)

    # Step 1: Shuffle (X, Y)
    permutation = list(np.random.permutation(m))
    shuffled_X = X[:, permutation]
    shuffled_Y = Y[:, permutation].reshape((Y.shape[0],m))

    # Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.
    num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning
    for k in range(0, num_complete_minibatches):
        mini_batch_X = shuffled_X[:, k * mini_batch_size : k * mini_batch_size + mini_batch_size]
        mini_batch_Y = shuffled_Y[:, k * mini_batch_size : k * mini_batch_size + mini_batch_size]
        mini_batch = (mini_batch_X, mini_batch_Y)
        mini_batches.append(mini_batch)

    # Handling the end case (last mini-batch < mini_batch_size)
    if m % mini_batch_size != 0:
        mini_batch_X = shuffled_X[:, num_complete_minibatches * mini_batch_size : m]
        mini_batch_Y = shuffled_Y[:, num_complete_minibatches * mini_batch_size : m]
        mini_batch = (mini_batch_X, mini_batch_Y)
        mini_batches.append(mini_batch)

    return mini_batches

def convert_to_one_hot(Y, C):
    Y = np.eye(C)[Y.reshape(-1)].T
    return Y

def predict(X, parameters):

    W1 = tf.convert_to_tensor(parameters["W1"])
    b1 = tf.convert_to_tensor(parameters["b1"])
    W2 = tf.convert_to_tensor(parameters["W2"])
    b2 = tf.convert_to_tensor(parameters["b2"])
    W3 = tf.convert_to_tensor(parameters["W3"])
    b3 = tf.convert_to_tensor(parameters["b3"])

    params = {"W1": W1,
              "b1": b1,
              "W2": W2,
              "b2": b2,
              "W3": W3,
              "b3": b3}

    x = tf.placeholder("float", [12288, 1])

    z3 = forward_propagation(x, params)
    p = tf.argmax(z3)

    with tf.Session() as sess:
        prediction = sess.run(p, feed_dict = {x: X})

    return prediction


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.
      In fact, the number of examples during test/train is different.
    """

    ### START CODE HERE ### (approx. 2 lines)
    X = tf.placeholder("float", [n_x, None])
    Y = tf.placeholder("float", [n_y, None])
    ### END CODE HERE ###

    return X, Y


def initialize_parameters():
    """
    Initializes parameters to build a neural network with tensorflow. The shapes are:
                        W1 : [25, 12288]
                        b1 : [25, 1]
                        W2 : [12, 25]
                        b2 : [12, 1]
                        W3 : [6, 12]
                        b3 : [6, 1]

    Returns:
    parameters -- a dictionary of tensors containing W1, b1, W2, b2, W3, b3
    """

    tf.set_random_seed(1)                              # so that your "random" numbers match ours

    ### START CODE HERE ### (approx. 6 lines of code)
    W1 = tf.get_variable("W1", [25,12288], initializer = tf.contrib.layers.xavier_initializer(seed = 1))
    b1 = tf.get_variable("b1", [25,1], initializer = tf.zeros_initializer())
    W2 = tf.get_variable("W2", [12,25], initializer = tf.contrib.layers.xavier_initializer(seed = 1))
    b2 = tf.get_variable("b2", [12,1], initializer = tf.zeros_initializer())
    W3 = tf.get_variable("W3", [6,12], initializer = tf.contrib.layers.xavier_initializer(seed = 1))
    b3 = tf.get_variable("b3", [6,1], initializer = tf.zeros_initializer())
    ### END CODE HERE ###

    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2,
                  "W3": W3,
                  "b3": b3}

    return parameters


def compute_cost(z3, Y):
    """
    Computes the cost

    Arguments:
    z3 -- output of forward propagation (output of the last LINEAR unit), of shape (10, number of examples)
    Y -- "true" labels vector placeholder, same shape as z3

    Returns:
    cost - Tensor of the cost function
    """

    # to fit the tensorflow requirement for tf.nn.softmax_cross_entropy_with_logits()
    logits = tf.transpose(z3)
    labels = tf.transpose(Y)

    ### START CODE HERE ### (1 line of code)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = labels))
    ### END CODE HERE ###

    return cost







def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,
          num_epochs = 1500, minibatch_size = 32, print_cost = True):
    """
    Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.

    Arguments:
    X_train -- training set, of shape (input size = 12288, number of training examples = 1080)
    Y_train -- test set, of shape (output size = 6, number of training examples = 1080)
    X_test -- training set, of shape (input size = 12288, number of training examples = 120)
    Y_test -- test set, of shape (output size = 6, number of test examples = 120)
    learning_rate -- learning rate of the optimization
    num_epochs -- number of epochs of the optimization loop
    minibatch_size -- size of a minibatch
    print_cost -- True to print the cost every 100 epochs

    Returns:
    parameters -- parameters learnt by the model. They can then be used to predict.
    """

    ops.reset_default_graph()                         # to be able to rerun the model without overwriting tf variables
    tf.set_random_seed(1)                             # to keep consistent results
    seed = 3                                          # to keep consistent results
    (n_x, m) = X_train.shape                          # (n_x: input size, m : number of examples in the train set)
    n_y = Y_train.shape[0]                                  # n_y : output size
    costs = []                                        # To keep track of the cost

    # Create Placeholders of shape (n_x, n_y)
    ### START CODE HERE ### (1 line)
    X, Y = create_placeholders(n_x, n_y)
    ### END CODE HERE ###

    # Initialize parameters
    ### START CODE HERE ### (1 line)
    parameters = initialize_parameters()
    ### END CODE HERE ###

    # Forward propagation: Build the forward propagation in the tensorflow graph
    ### START CODE HERE ### (1 line)
    z3 = forward_propagation(X, parameters)
    ### END CODE HERE ###

    # Cost function: Add cost function to tensorflow graph
    ### START CODE HERE ### (1 line)
    cost = compute_cost(z3, Y)
    ### END CODE HERE ###

    # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.
    ### START CODE HERE ### (1 line)
    optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)
    ### END CODE HERE ###

    # Initialize all the variables
    init = tf.global_variables_initializer()

    # Start the session to compute the tensorflow graph
    with tf.Session() as sess:

        # Run the initialization
        sess.run(init)

        # Do the training loop
        for epoch in range(num_epochs):

            minibatch_cost = 0.
            num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set
            seed = seed + 1
            minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)

            for minibatch in minibatches:

                # Select a minibatch
                (minibatch_X, minibatch_Y) = minibatch

                # IMPORTANT: The line that runs the graph on a minibatch.
                # Run the session to execute the optimizer and the cost, the feedict should contain a minibatch for (X,Y).
                ### START CODE HERE ### (1 line)
                _ , temp_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})
                ### END CODE HERE ###

                minibatch_cost += temp_cost / num_minibatches

            # Print the cost every epoch
            if print_cost == True and epoch % 100 == 0:
                print ("Cost after epoch %i: %f" % (epoch, minibatch_cost))
            if print_cost == True and epoch % 5 == 0:
                costs.append(minibatch_cost)

        # plot the cost
        plt.plot(np.squeeze(costs))
        plt.ylabel('cost')
        plt.xlabel('iterations (per tens)')
        plt.title("Learning rate =" + str(learning_rate))
        plt.show()

        # lets save the parameters in a variable
        parameters = sess.run(parameters)
        print ("Parameters have been trained!")

        # Calculate the correct predictions
        correct_prediction = tf.equal(tf.argmax(z3), tf.argmax(Y))

        # Calculate accuracy on the test set
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

        print ("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train}))
        print ("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test}))

        return parameters
Previoustensorflow tutorialNexttf_utils.py

Last updated 6 years ago

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