吴恩达深度学习课程资料（含部分代码）

吴恩达老师的深度学习课程资料。

import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops

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） （m Hi Wi Ci）
    Y -- true “label“ vector （containing 0 if cat 1 if non-cat） of shape （1 number of examples） （m n_y）
    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[0]                  # 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:]

    # 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 forward_propagation_for_predict（X parameters）:
    “““
    Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RE

 属性            大小     日期    时间   名称
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     目录           0  2018-06-08 10:27  deeplearning.ai-master\
     目录           0  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\
     文件         247  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\README.md
     目录           0  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week1\
     文件       87116  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week1\Convolution+model+-+Application+-+v1.ipynb
     文件       58623  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week1\Convolution+model+-+Step+by+Step+-+v2.ipynb
     文件          76  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week1\README.md
     文件        5635  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week1\cnn_utils.py
     目录           0  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week1\datasets\
     文件          18  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week1\datasets\README.md
     目录           0  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week2\
     文件       79312  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week2\Keras+-+Tutorial+-+Happy+House+v2.ipynb
     文件         210  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week2\README.md
     文件      347116  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week2\Residual+Networks+-+v2.ipynb
     文件        4723  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week2\resnets_utils.py
     目录           0  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\
     文件      247418  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\Autonomous+driving+application+-+Car+detection+-+v1.ipynb
     文件         437  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\Drive.ai+Dataset+Sample+LICENSE
     文件        1827  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\LICENSE
     文件         148  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\README.md
     目录           0  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\font\
     文件      127344  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\font\FiraMono-Medium.otf
     文件        4434  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\font\SIL+Open+Font+License.txt
     目录           0  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\model_data\
     文件         625  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\model_data\coco_classes.txt
     文件           3  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\model_data\object_classes.txt
     文件          90  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\model_data\yolo_anchors.txt
     目录           0  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\yad2k\
     目录           0  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\yad2k\models\
     文件        2388  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\yad2k\models\keras_darknet19.py
     文件       16614  2018-06-08 10:27  deeplearning.ai-master\Convolutional Neural Networks\week3\yad2k\models\keras_yolo.py
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