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?->?LINEA

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2018-04-25?17:32??datasets\
?????文件?????1477712??2018-04-16?15:39??datasets\test_signs.h5
?????文件????13281872??2018-04-16?15:38??datasets\train_signs.h5
?????文件????????4785??2018-04-25?11:07??cnn_utils.py