質問編集履歴
2
データセットの準備部分についてのコードを付け加えました。
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```python
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# 前データ処理関連
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import pickle
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import numpy as np
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# データセットのダウンロード
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dtype = 'float16'
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def unpickle(file):
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import pickle
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with open(file, 'rb') as fo:
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dict = pickle.load(fo, encoding='bytes')
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return dict
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batch_name = { 1: "data_batch_1", 2:"data_batch_2", 3: "data_batch_3", 4: "data_batch_4", 5: "data_batch_5"}
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A = []
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for (a,b) in batch_name.items():
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tmp = unpickle("cifar-10-batches-py/{}".format(b))
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A.append(tmp)
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40
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tmp = unpickle("cifar-10-batches-py/test_batch")
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X_test, Y_test = np.array(tmp[b'data'], dtype=dtype), np.array(tmp[b'labels'], dtype=dtype)
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Y = np.array([])
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X = np.array([[]])
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for i in range(5):
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50
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if i == 0:
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X = np.array(A[i][b'data'], dtype=dtype)
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Y = np.array(A[i][b'labels'], dtype=dtype)
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else:
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X = np.append(X, np.array(A[i][b'data'], dtype=dtype), axis=0)
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Y = np.append(Y, np.array(A[i][b'labels'], dtype=dtype))
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# データセットの整理
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X_image = X.reshape(-1, 3, 1024).copy()
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X_max = np.amax(X_image, axis=2, keepdims=True)
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X_min = np.amin(X_image, axis=2, keepdims=True)
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X_mean = X_image.mean(axis=2, dtype=dtype, keepdims=True)
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X_image_st = (X_image - X_mean)/(X_max-X_min)
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# testデータの方も同様の操作を行う
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X_test_image = X_test.reshape(-1, 3, 1024).copy()
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X_test_max = np.amax(X_test_image, axis=2, keepdims=True)
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X_test_min = np.amin(X_test_image, axis=2, keepdims=True)
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X_test_mean = X_test_image.mean(axis=2, dtype=dtype, keepdims=True)
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X_test_image_st = (X_test_image - X_test_mean)/(X_test_max-X_test_min)
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# RGBの順番にデータが並ぶように結合を直す(ここは勘違い
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88
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# の可能性があります)
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X_image_st2 = X_image_st.reshape(-1, 3, 1024,1)
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X_train = np.concatenate((X_image_st2[:,0], X_image_st2[:,1], X_image_st2[:, 2]), axis=2)
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X_test_image_st2 = X_test_image_st.reshape(-1,3, 1024,1)
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X_test = np.concatenate((X_test_image_st2[:,0], X_test_image_st2[:,1], X_test_image_st2[:, 2]), axis=2)
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X_train = X_train.reshape(-1, 1024, 3)
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X_test = X_test.reshape(-1, 1024, 3)
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# ラベルデータの前準備(0~9の値からそのインデックスが1になる
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# ような2次元配列を生成する
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Y_test2 = np.zeros((10000, 10))
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Y_train = np.zeros((50000, 10))
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for i in range(10000):
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Y_test2[i, int(Y_test[i])] = 1
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for i in range(50000):
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Y_train[i, int(Y[i])] = 1
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import tensorflow as tf
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x = tf.placeholder(tf.half, shape=[None, 1024, 3])
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1
エラー文を追加しました
test
CHANGED
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File without changes
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test
CHANGED
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@@ -178,4 +178,10 @@
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[[Node: mul_1 = Mul[T=DT_HALF, _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_Placeholder_8_0_2, Log_1)]]
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InvalidArgumentError: Incompatible shapes: [50,10] vs. [25,10]
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[[Node: mul_1 = Mul[T=DT_HALF, _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_Placeholder_8_0_2, Log_1)]]
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```
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