tf-使用cnn深入mnist

實現高效運算

爲了用python 實現高效的數值運算，常常會使用如numpy類的函數庫，會把類似矩陣乘法這樣的複雜運算使用其他外部語言來實現。但從外部計算切回Python的每一個操作，仍然是一個很大的開銷。tf爲了避免這些開銷，故不單獨的運行單一的複雜運算，而是先用圖來描述一系列計算操作，然後全部放在一起到外部運行。

交叉熵

mnist訓練使用交叉熵表示成本（cost），定義爲：

Hytest(y)=−∑iytestilog(yi)

api

tf.argmax(input, dimension, name=None)
可以幫助評估。它的三個輸入分別是：

• input：輸入的張量，在本例中就是樣本的標籤張量或預測張量。
• demension：這裏是一維
• name：給這個部件起個名字。。可以不用起。
  這個函數的作用是給出預測出來的10個結果（對應的每個類別的分類概率）中的最大值的下標。

tf.cast(x, dtype, name=None)

將x轉換爲對應的dtype數據類型

tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)

tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)

1、從截斷的正態分佈中輸出隨機值。
生成的值服從具有指定平均值和標準偏差的正態分佈，如果生成的值大於平均值2個標準偏差的值則丟棄重新選擇。
2、從正態分佈中輸出隨機值。
參數:

• shape: 一維的張量，也是輸出的張量。
• mean: 正態分佈的均值。
• stddev: 正態分佈的標準差。
• dtype: 輸出的類型。
• seed: 一個整數，當設置之後，每次生成的隨機數都一樣。
• name: 操作的名字

輸出結果

step 0, training accuracy 0.12
step 100, training accuracy 0.84
step 200, training accuracy 0.9
step 300, training accuracy 0.82
step 400, training accuracy 1
step 500, training accuracy 0.94
step 600, training accuracy 1
step 700, training accuracy 1
step 800, training accuracy 0.9
step 900, training accuracy 1
step 1000, training accuracy 0.96
step 1100, training accuracy 0.98
step 1200, training accuracy 0.92
step 1300, training accuracy 0.98
step 1400, training accuracy 1
step 1500, training accuracy 0.92
step 1600, training accuracy 1
step 1700, training accuracy 1
step 1800, training accuracy 0.98
step 1900, training accuracy 0.94
step 2000, training accuracy 1
step 2100, training accuracy 1
step 2200, training accuracy 1
step 2300, training accuracy 1
step 2400, training accuracy 1
step 2500, training accuracy 1
step 2600, training accuracy 1
step 2700, training accuracy 0.98
step 2800, training accuracy 0.98
step 2900, training accuracy 0.96
step 3000, training accuracy 0.98
step 3100, training accuracy 1
step 3200, training accuracy 0.98
step 3300, training accuracy 1
step 3400, training accuracy 1
step 3500, training accuracy 0.98
step 3600, training accuracy 0.96
step 3700, training accuracy 1
step 3800, training accuracy 0.94
step 3900, training accuracy 1
step 4000, training accuracy 0.92
step 4100, training accuracy 1
step 4200, training accuracy 0.98
step 4300, training accuracy 0.98
step 4400, training accuracy 1
step 4500, training accuracy 1
step 4600, training accuracy 0.98
step 4700, training accuracy 1
step 4800, training accuracy 1
step 4900, training accuracy 0.98
step 5000, training accuracy 1
step 5100, training accuracy 1
step 5200, training accuracy 1
step 5300, training accuracy 0.98
step 5400, training accuracy 0.98
step 5500, training accuracy 1
step 5600, training accuracy 0.98
step 5700, training accuracy 0.96
step 5800, training accuracy 0.98
step 5900, training accuracy 1
step 6000, training accuracy 1
step 6100, training accuracy 1
step 6200, training accuracy 0.96
step 6300, training accuracy 0.98
step 6400, training accuracy 0.98
step 6500, training accuracy 1
step 6600, training accuracy 1
step 6700, training accuracy 1
step 6800, training accuracy 0.98
step 6900, training accuracy 1
step 7000, training accuracy 1
step 7100, training accuracy 1
step 7200, training accuracy 1
step 7300, training accuracy 0.98
step 7400, training accuracy 1
step 7500, training accuracy 0.96
step 7600, training accuracy 1
step 7700, training accuracy 1
step 7800, training accuracy 1
step 7900, training accuracy 1
step 8000, training accuracy 1
step 8100, training accuracy 0.98
step 8200, training accuracy 1
step 8300, training accuracy 1
step 8400, training accuracy 0.98
step 8500, training accuracy 1
step 8600, training accuracy 1
step 8700, training accuracy 1
step 8800, training accuracy 1
step 8900, training accuracy 1
step 9000, training accuracy 1
step 9100, training accuracy 1
step 9200, training accuracy 1
step 9300, training accuracy 1
step 9400, training accuracy 1
step 9500, training accuracy 1
step 9600, training accuracy 0.98
step 9700, training accuracy 0.98
step 9800, training accuracy 1
step 9900, training accuracy 1
step 10000, training accuracy 1
step 10100, training accuracy 1
step 10200, training accuracy 1
step 10300, training accuracy 1
step 10400, training accuracy 1
step 10500, training accuracy 1
step 10600, training accuracy 1
step 10700, training accuracy 1
step 10800, training accuracy 1
step 10900, training accuracy 1
step 11000, training accuracy 1
step 11100, training accuracy 1
step 11200, training accuracy 1
step 11300, training accuracy 0.98
step 11400, training accuracy 1
step 11500, training accuracy 1
step 11600, training accuracy 1
step 11700, training accuracy 1
step 11800, training accuracy 1
step 11900, training accuracy 1
step 12000, training accuracy 0.98
step 12100, training accuracy 1
step 12200, training accuracy 1
step 12300, training accuracy 1
step 12400, training accuracy 1
step 12500, training accuracy 1
step 12600, training accuracy 0.98
step 12700, training accuracy 1
step 12800, training accuracy 1
step 12900, training accuracy 1
step 13000, training accuracy 1
step 13100, training accuracy 1
step 13200, training accuracy 1
step 13300, training accuracy 1
step 13400, training accuracy 0.98
step 13500, training accuracy 1
step 13600, training accuracy 1
step 13700, training accuracy 1
step 13800, training accuracy 0.98
step 13900, training accuracy 1
step 14000, training accuracy 1
step 14100, training accuracy 1
step 14200, training accuracy 1
step 14300, training accuracy 1
step 14400, training accuracy 1
step 14500, training accuracy 1
step 14600, training accuracy 1
step 14700, training accuracy 0.98
step 14800, training accuracy 1
step 14900, training accuracy 1
step 15000, training accuracy 1
step 15100, training accuracy 1
step 15200, training accuracy 1
step 15300, training accuracy 0.98
step 15400, training accuracy 1
step 15500, training accuracy 0.98
step 15600, training accuracy 1
step 15700, training accuracy 1
step 15800, training accuracy 1
step 15900, training accuracy 1
step 16000, training accuracy 1
step 16100, training accuracy 1
step 16200, training accuracy 1
step 16300, training accuracy 1
step 16400, training accuracy 1
step 16500, training accuracy 1
step 16600, training accuracy 1
step 16700, training accuracy 1
step 16800, training accuracy 1
step 16900, training accuracy 0.98
step 17000, training accuracy 1
step 17100, training accuracy 1
step 17200, training accuracy 1
step 17300, training accuracy 1
step 17400, training accuracy 1
step 17500, training accuracy 1
step 17600, training accuracy 1
step 17700, training accuracy 1
step 17800, training accuracy 1
step 17900, training accuracy 1
step 18000, training accuracy 1
step 18100, training accuracy 1
step 18200, training accuracy 1
step 18300, training accuracy 1
step 18400, training accuracy 1
step 18500, training accuracy 1
step 18600, training accuracy 1
step 18700, training accuracy 1
step 18800, training accuracy 1
step 18900, training accuracy 0.98
step 19000, training accuracy 1
step 19100, training accuracy 1
step 19200, training accuracy 1
step 19300, training accuracy 1
step 19400, training accuracy 1
step 19500, training accuracy 1
step 19600, training accuracy 1
step 19700, training accuracy 1
step 19800, training accuracy 1
step 19900, training accuracy 1
test accuracy 0.992

padding

same 補0，輸出維度等於輸出的圖像維度/strides
valid 不補，輸出維度等於輸入維度（d-patchsize + 1）/strides