娣卞害瀛︿範_1_Tensorflow_1
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# 娣卞害瀛︿範 # 鍥惧儚璇嗗埆,鑷劧璇█澶勭悊 # 鏈哄櫒瀛︿範 娣卞害瀛︿範 # 鍒嗙被:绁炵粡缃戠粶(绠€鍗? 绁炵粡缃戠粶(娣卞害) # 鍥炲綊 鍥惧儚:鍗风Н绁炵粡缃戠粶 # 鑷劧璇█澶勭悊:寰幆绁炵粡缃戠粶 # cpu:杩愯鎿嶄綔绯荤粺,澶勭悊涓氬姟,璁＄畻鑳藉姏涓嶆槸鐗瑰埆绐佸嚭 # gpu:涓撻棬涓鸿绠楄璁＄殑 import tensorflow as tf a = tf.constant(5.0) b = tf.constant(6.0) sum1 = tf.add(a,b) # 鍦╯ession澶栬竟鎵撳嵃鏃跺彧鑳芥煡鐪嬪璞?/span> # 绋嬪簭鐨勫浘 a,b,sum1涔熸湁graph graph = tf.get_default_graph() print(a.graph) print(graph) # session()杩愯榛樿鐨勫浘,褰撹繍琛岀殑鍏冪礌涓嶆槸榛樿鍥剧殑鏃跺€?浼氭姤閿?/span> with tf.Session() as sess: print(sess.run(sum1)) # 杈撳嚭鍊?/span> # 鍒涘缓鏂扮殑鍥?/span> g = tf.Graph() with g.as_default(): c = tf.constant(11.0) print(c.graph) # 涓庝笂杈圭殑鍥句笉鍚?/span> # 鍥剧▼搴忕殑绌洪棿,鍙橀噺,绾跨▼绛夎祫婧愰兘鍦ㄥ浘涓?/span> # 浼氳瘽杩愯鍥剧殑绋嬪簭, # tf.Session(graph=c) 鎸囧畾鍥捐繍琛? 閲岃竟run鐨勬椂鍊欒娉ㄦ剰 # session.run鐨勪綔鐢?鍚姩鏁翠釜鍥?/span> # session.close:鍏抽棴,閲婃斁璧勬簮娌?/span> # Session涓殑鍙傛暟 # tf.Session(config=tf.ConfigProto(log_device_placement=True)) # 浜や簰寮弒ession:tf.InteractiveSession() # 璋冪敤鍚?涓嶇敤Session() 涓嶅悓run 鐩存帴a.eval()涔熷彲 # 鍏跺疄鍙鏈変細璇濈殑涓婁笅鏂囩幆澧?灏卞彲浠ヤ娇鐢╡val() # =================================================== # 浼氳瘽鐨剅un() # run(fetches,feed_dict=None,graph=None) 杩愯ops涓巘ensor # fetches 闇€瑕乺un鐨勫唴瀹?鏈夊涓椂浣跨敤[] # 涓嶆槸op涓嶈兘run 渚?sum2 = 1+3 # 浣?sum3=1+tf.constant(3.0) 鍙互run(sum3) # ======================================== # 瀹炴椂鎻愪緵鏁版嵁 # placeholder # tf.placeholder(dtype,shape=None,name=None) # plt = tf.placeholder(tf.float32,[2,3]) [None,3]涔熷彲 # run(plt,feed_dict={plt:[[1,2,3],[4,5,6]]}) input1 = tf.placeholder(tf.float32) # 鍙互璇存槸涓€涓崰浣嶇,浣跨敤鐨勬椂鍊欓渶瑕佷紶鍏ュ€?/span> input2 = tf.placeholder(tf.float32) output = input1*input2 with tf.Session() as sess: # 浼犲€肩殑鏃跺€欎娇鐢╢eed_dict 瀛楀吀 鍗犱綅绗﹀璞′綔涓洪敭,鍊奸渶瑕佷娇鐢╗] 鍖呭惈 print(sess.run(output,feed_dict={input1:[7],input2:[2.6]})) # ============================================================= # 寮犻噺tensor # 灏唍umpy涓殑鏁扮粍灏佽涓簍ensor绫诲瀷 # tensor:鍚嶅瓧,shape,dtype # 闃?缁村害 # 鏁版嵁绫诲瀷:tf.float32,64(鍏跺疄娌℃湁鎰忎箟,瀹為檯杩樻槸32) int8-64,uint8,string,bool # print(a.shape,a.name,a.op,a.graph) # 0缁?() 1缁?(n) 2缁?(n,m) ... # ====================================== # Numpy:reshape 鎶婂師鏉ョ殑鏁版嵁鐩存帴淇敼 # tensorflow涓?/span> # tf.reshape:鍒涘缓鏂扮殑寮犻噺 鍔ㄦ€佸舰鐘?/span> # tf.Tensor.set_shape:鏇存柊Tensor鐨勯潤鎬佸舰鐘?/span> # 闈欐€佸舰鐘?(褰撴暟閲忎笉纭畾鏃跺彲浠?鍒囦笉鑳借法缁村害) plt = tf.placeholder(tf.float32,[None,2]) # shape=(?,2) plt.set_shape([3,2]) # shape=(3,2) plt.set_shape([4,2]) # 姝ゆ椂涓嶈兘淇敼 # 鍔ㄦ€佸舰鐘?(娉ㄦ剰鍏冪礌涓暟涓嶈兘鏀瑰彉,鍙法缁村害) new_plt=tf.reshape(plt,[2,3]) # shape=(2,3) # ========================================== # 鏈夐粯璁ゅ€肩殑寮犻噺 # tf.zero(shape,dtype=tf.float32,name=None) 鍏ㄤ负0 # tf.ones(shape,dtype=float32,name=None) 鍏ㄤ负1 # tf.constant(value,dtype=None,shape=None,name=None) 甯搁噺寮犻噺 # tf.random_normal(shape,mean=0.0,stddev=1.0,dtype=float32,seed=None,name=None) 鐢辨澶垎閮ㄧ殑闅忔満鍊肩粍鎴愮殑鐭╅樀 # ========================== # 寮犻噺鐨勭被鍨嬪彉鎹?/span> # tf.string_to_number(string_tensor,out_type=None,name=None) 绛?/span> # tf.cost(x,dtype,name=None) 涓囪兘杞崲 # tf.cost(鍘熸潵鏁版嵁,鏂扮被鍨? # =========================== # 鏁版嵁鎷兼帴 a=[[1,2,3],[4,5,6]] b = [[7,8,9],[10,11,12]] # tf.concat([a,b],axis=1) 鍚堝苟鍚庡彉涓?鍒?/span> # api https://www.tensorflow.org/versions/r1.0/api_guides/python/math_ops # =================================================== # 鍙橀噺op 鍙互鎸佷箙鍖? 鏅€氱殑寮犻噺op涓嶈 # 鍙橀噺op闇€瑕佸湪浼氳瘽涓繍琛屽垵濮嬪寲 # name鍙傛暟:鍦╰ensorboard涓樉绀哄悕瀛?鍙互璁╃浉鍚宱p鍚嶅瓧鐨勬暟鎹繘琛屽尯鍒?/span> # 璁剧疆鍚?Tensor("Variable") ---->Tensor("璁剧疆鐨刵ame") a = tf.constant([1,2,3,4,5]) random = tf.random_normal([2,3],mean=0.0,stddev=1.0) var = tf.Variable(initial_value=random,name=None,trainable=None) print(a,var) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) # 鍒濆鍖杘p print(sess.run([a,var])) # 鍐嶆鎵撳嵃 # ==================================================== # 鍙鍖?/span> # 妯″潡:summary with tf.Session() as sess: sess.run(init) filewriter = tf.summary.FileWriter(".",graph=sess.graph) # 杩愯鍚庣敓鎴愭枃浠?姣忔杩愯閮戒細鐢熸垚鏂囦欢, # tensorboard --logdir="鐢熸垚鐨勬枃浠舵墍鍦ㄧ殑鐩綍" 浼氬惎鍔ㄤ竴涓湇鍔″櫒,璁块棶鍗冲彲 # =============================================== # 绾挎€у洖褰掑師鐞嗗強瀹炵幇 # 1,杞濂界壒寰佸拰鐩爣鍊?/span> # 2,寤虹珛妯″瀷 妯″瀷鍙傛暟蹇呴』鏄彉閲?/span> # 3,姹傛崯澶卞嚱鏁?璇樊 鍧囨柟璇樊 # 4,姊害涓嬮檷浼樺寲鎹熷け杩囩▼,鎸囧畾瀛︿範鐜?/span> # ========杩愮畻api # tf.matmul(x,w) 鐭╅樀杩愮畻 # tf.squqre(error) 骞虫柟 姣忎釜鏍锋湰璇樊骞虫柟 # tf.reduce_mean(error) 姣忎釜鍒楄〃骞冲潎鍊?/span> # ===========姊害涓嬮檷api # tf.train.GradientDescentOptimizer(learning_rate) # minimize # 杩斿洖姊害涓嬮檷op # ============================================== # tensorflow 瀹炵幇绠€鍗曠殑绾挎€у洖褰?/span> import tensorflow as tf def myregression(): """ 鑷疄鐜颁竴涓嚎鎬у洖褰掗娴? """ # 1,鍑嗗鏁版嵁, x 鐗瑰緛鍊糩100,10] y鐩爣鍊糩100] # 鍑嗗x x = tf.random_normal([100,1],mean=1.75,stddev=0.5,name="x_data") # 鍑嗗y,鑷畾涔夊嚭瀹為檯鐨剋,b # 鐭╅樀鐩镐箻蹇呴』鏄簩缁寸殑 y_true= tf.matmul(x,[[0.7]])+0.8 # 2,寤虹珛妯″瀷 # 闅忔満鐨勬潈閲嶄笌鍋忕疆,璁╄繘琛屼紭鍖?/span> # 鍙兘浣跨敤鍙橀噺瀹氫箟,trainable鎺у埗璇ュ彉閲?璁粌鐨勬椂鍊欐槸鍚﹁鍙樺寲 weight = tf.Variable(tf.random_normal([1,1],mean=0.0,stddev=0.75),trainable=True) bias = tf.Variable(0.0,name="b") y_predcit = tf.matmul(x,weight)+bias # 3,寤虹珛鎹熷け鍑芥暟,鍧囨柟璇樊 loss = tf.reduce_mean(tf.square(y_true-y_predcit)) # 4,姊害涓嬮檷浼樺寲鎹熷け youhua = tf.train.GradientDescentOptimizer(0.1) # 涓€鑸?-1涔嬮棿涓嶈兘澶ぇ, # 涔熷彲2,3,10绛?鑻ュお澶у彲鑳戒細鍑虹幇nan:姊害鐖嗙偢 # 瑙ｅ喅鏂规:閲嶆柊璁捐缃戠粶,璋冩暣瀛︿範鐜?浣跨敤姊害鎴柇,浣跨敤婵€娲诲嚱鏁?/span> train_op = youhua.minimize(loss) # 鏀堕泦tensor tf.summary.scalar("losses",loss) # 鍦╰ensorborad涓?scalars 浼氭樉绀哄湪瀛︿範鐨勮繃绋嬩腑loss鐨勫彉鍖栨洸绾?/span> tf.summary.histogram("weights",weight) # 瀹氫箟鍚堝苟tensor鐨刼p merged=tf.summary.merge_all() saver = tf.train.Saver() init = tf.global_variables_initializer() # 浼氳瘽杩愯 with tf.Session() as sess: sess.run(init) # 鎵撳嵃涓嶄紭鍖栫殑train_op print(sess.run([weight,bias])) filewrite = tf.summary.FileWriter(".",graph=sess.graph) # 妯″瀷鎭㈠ # 妯″瀷鏂囦欢瀛樺湪 # saver.restore("sess","璺緞") # 寰幆杩愯浼樺寲 for i in range(1000): sess.run(train_op) # 杩愯鍚堝苟鐨則ensor summary = sess.run(merged) filewrite.add_summary(summary,i) print("绗瑊}娆?/span>".format(i),sess.run([weight, bias])) saver.save(sess,"./reserve/model") if __name__ =="__main__": myregression() # ======================== # tensorflow鍙橀噺浣滅敤鍩焧f.variable_scope()鍒涘缓鎸囧畾鍚嶅瓧鐨勫彉閲忎綔鐢ㄥ煙 # 涓嶅悓鐨勯儴鍒嗘斁鍦ㄤ笉鍚岀殑浣滅敤鍩熶笅,tensorflowboard涓璯raph 浼氭洿鍔犳竻鏅?浣滅敤鍒嗘槑 with tf.variable_scope("name"): pass # 澧炲姞鍙橀噺鏄剧ず # 娣诲姞鏉冮噸鍙傛暟,鎹熷け鍊肩瓑鍦╰ensorborad涓樉绀?/span> # 1,鏀堕泦鍙橀噺 # tf.summary.scalar(name="",tensir)鏀堕泦瀵逛簬鎹熷け鍑芥暟鍜屽噯纭巼绛夊崟鍊煎彉閲?name涓哄彉閲忓€?tensor涓哄€?/span> # tf.summary.histogram(name="",tensor) 鏀堕泦楂樼淮搴︾殑鍙橀噺鍙傛暟 # 2,鍚堝苟鍙橀噺鍐欏叆浜嬩欢鏂囦欢 # merged = tf.summary.merge_all() # 杩愯鍚堝苟:summary=sess.run(merged) 姣忔杩唬閮介渶瑕佽繍琛?/span> # 娣诲姞:FileWriter.add_summary(summary,i)i琛ㄧず绗嚑娆¤凯浠?/span> # ======================== # tensorflow鍙橀噺浣滅敤鍩焧f.variable_scope()鍒涘缓鎸囧畾鍚嶅瓧鐨勫彉閲忎綔鐢ㄥ煙 # 涓嶅悓鐨勯儴鍒嗘斁鍦ㄤ笉鍚岀殑浣滅敤鍩熶笅,graph 浼氭洿鍔犳竻鏅?浣滅敤鍒嗘槑 with tf.variable_scope("name"): pass # 妯″瀷鐨勪繚瀛樹笌鍔犺浇 saver = tf.train.Saver(var_list=None,max_to_keep=5) # var_list:鎸囧畾瑕佷繚瀛樺拰杩樺師鐨勫彉閲?浣滀负涓€涓猟ict鎴栧垪琛ㄤ紶閫?/span> # max_to_keep:鎸囩ず瑕佷繚鐣欑殑鏈€杩戞鏌ョ偣鏂囦欢鐨勬渶澶ф暟閲?鍒涘缓鏂版枃浠舵椂,鍒犻櫎鏃ф枃浠?淇濈暀鏈€鏂扮殑5涓?/span> # 鏂囦欢鏍煎紡:checkpoint鏂囦欢 saver.save("sess瀵硅薄","璺緞/鏂囦欢鍚嶅瓧") # 绗竴娆′繚瀛?/span> # checkpoint:璁板綍妯″瀷鍚嶅瓧,鏂囦欢璺緞 # name.data-00000-of-00001 鏁版嵁瀛樺偍鏂囦欢 # name.index name.meta # 妯″瀷鐨勫姞杞?/span> # saver.restore(sess,"璺緞") # 鍦╳ith鏀惧叆浼氳瘽涓?寮€濮嬩紭鍖栧墠 # =================================== # 鑷畾涔夊懡浠よ鍙傛暟 # 1, 棣栧厛瀹氫箟鏈夊摢浜涘弬鏁伴渶瑕佸湪杩愯鏃舵寚瀹?/span> # 2,绋嬪簭褰撲腑鑾峰彇瀹氫箟鐨勫懡浠よ鍙傛暟 # 鍚嶅瓧,榛樿鍊?璇存槑 # 浠ュ墠鐨勭増鏈?/span> # tf.app.flags.DEFINE_integer("max_step",100,"妯″瀷璁粌鐨勬鏁?) # tf.app.flags.FLAGS.max_step 鑾峰彇鏁版嵁 # 鏂扮増 flags = tf.flags.FLAGS # 瀹氫箟瀵硅薄 tf.flags.DEFINE_integer("max_step",100,"妯″瀷璁粌鐨勬鏁?/span>") tf.flags.DEFINE_string("file_path","","鏂囦欢璺緞") tf.flags._FlagValuesWrapper # 鍒濆鍖?/span> flags.max_step=100 # 淇敼 鎴栬幏鍙?/span> # 瀹氫箟瀹屾垚鍚? 杩愯鏂囦欢鏃?python xx.py --max-step=500 鍗冲彲浼犲叆,瀛楃涓查渶瑕佸姞寮曞彿
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