質問編集履歴
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実行できているソースなどをすべて記載。
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```python
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#ラベリングによる学習/検証データの準備
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#import os
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#os.environ['KERAS_BACKEND']='tensorflow'
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#
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from PIL import Image
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import os, glob
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import numpy as np
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import random, math
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#画像が保存されているルートディレクトリのパス
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root_dir = "C:/animal_pic"
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# 商品名
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categories = ["pos",
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"neg"]
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# 画像データ用配列
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X = []
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# ラベルデータ用配列
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Y = []
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#画像データごとにadd_sample()を呼び出し、X,Yの配列を返す関数
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def make_sample(files):
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global X, Y
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X = []
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Y = []
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for cat, fname in files:
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add_sample(cat, fname)
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return np.array(X), np.array(Y)
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#渡された画像データを読み込んでXに格納し、また、
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#画像データに対応するcategoriesのidxをY格納する関数
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def add_sample(cat, fname):
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img = Image.open(fname)
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img = img.convert("RGB")
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#img = img.resize((150, 150))
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data = np.asarray(img)
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X.append(data)
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Y.append(cat)
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#全データ格納用配列
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allfiles = []
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#カテゴリ配列の各値と、それに対応するidxを認識し、全データをallfilesにまとめる
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for idx, cat in enumerate(categories):
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image_dir = root_dir + "/" + cat
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files = glob.glob(image_dir + "/*.tif")
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for f in files:
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allfiles.append((idx, f))
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#シャッフル後、学習データと検証データに分ける
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random.shuffle(allfiles)
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th = math.floor(len(allfiles) * 0.8)
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train = allfiles[0:th]
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test = allfiles[th:]
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X_train, y_train = make_sample(train)
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X_test, y_test = make_sample(test)
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xy = (X_train, X_test, y_train, y_test)
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#データを保存する
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np.save("animal_data.npy", xy)
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```
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###実行結果
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表示なし
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###
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```python
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#モデルの構築
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from keras import layers, models
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model = models.Sequential()
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model.add(layers.Conv2D(32,(3,3),activation="relu",input_shape=(150,150,3)))
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model.add(layers.MaxPooling2D((2,2)))
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model.add(layers.Conv2D(64,(3,3),activation="relu"))
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model.add(layers.MaxPooling2D((2,2)))
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model.add(layers.Conv2D(128,(3,3),activation="relu"))
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model.add(layers.MaxPooling2D((2,2)))
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model.add(layers.Conv2D(128,(3,3),activation="relu"))
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model.add(layers.MaxPooling2D((2,2)))
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model.add(layers.Flatten())
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model.add(layers.Dense(512,activation="relu"))
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model.add(layers.Dense(2,activation="sigmoid")) #分類先の種類分設定
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#モデル構成の確認
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model.summary()
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```
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### 実行結果
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_________________________________________________________________
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Layer (type) Output Shape Param #
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=================================================================
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conv2d_5 (Conv2D) (None, 148, 148, 32) 896
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_________________________________________________________________
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max_pooling2d_5 (MaxPooling2 (None, 74, 74, 32) 0
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_________________________________________________________________
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conv2d_6 (Conv2D) (None, 72, 72, 64) 18496
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_________________________________________________________________
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max_pooling2d_6 (MaxPooling2 (None, 36, 36, 64) 0
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_________________________________________________________________
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conv2d_7 (Conv2D) (None, 34, 34, 128) 73856
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_________________________________________________________________
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max_pooling2d_7 (MaxPooling2 (None, 17, 17, 128) 0
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_________________________________________________________________
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conv2d_8 (Conv2D) (None, 15, 15, 128) 147584
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_________________________________________________________________
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max_pooling2d_8 (MaxPooling2 (None, 7, 7, 128) 0
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_________________________________________________________________
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flatten_2 (Flatten) (None, 6272) 0
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_________________________________________________________________
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dense_3 (Dense) (None, 512) 3211776
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_________________________________________________________________
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dense_4 (Dense) (None, 2) 1026
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=================================================================
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Total params: 3,453,634
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Trainable params: 3,453,634
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Non-trainable params: 0
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###
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```python
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#モデルのコンパイル
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from keras import optimizers
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model.compile(loss="binary_crossentropy",
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optimizer=optimizers.RMSprop(lr=1e-4),
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metrics=["acc"])
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```
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###実行結果
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表示なし
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###
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```python
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#データの準備
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from keras.utils import np_utils
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import numpy as np
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categories = ["pos","neg"]
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nb_classes = len(categories)
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X_train, X_test, y_train, y_test = np.load("/animal_data.npy")
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#データの正規化
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X_train = X_train.astype("float") / 255
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X_test = X_test.astype("float") / 255
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#kerasで扱えるようにcategoriesをベクトルに変換
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y_train = np_utils.to_categorical(y_train, nb_classes)
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y_test = np_utils.to_categorical(y_test, nb_classes)
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```
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###実行結果
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表示なし
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###
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```python
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#モデルの学習
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```
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実行結果 ここでエラーがでてます。
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---------------------------------------------------------------------------
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ValueError Traceback (most recent call last)
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<ipython-input-5-a383b561d91a> in <module>
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4 epochs=10,
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5 batch_size=6,
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----> 6 validation_data=(X_test,y_test))
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C:\ProgramData\Anaconda3\envs\PythonAI\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
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950 sample_weight=sample_weight,
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951 class_weight=class_weight,
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--> 952 batch_size=batch_size)
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953 # Prepare validation data.
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954 do_validation = False
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C:\ProgramData\Anaconda3\envs\PythonAI\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
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749 feed_input_shapes,
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750 check_batch_axis=False, # Don't enforce the batch size.
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--> 751 exception_prefix='input')
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753 if y is not None:
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C:\ProgramData\Anaconda3\envs\PythonAI\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
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126 ': expected ' + names[i] + ' to have ' +
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127 str(len(shape)) + ' dimensions, but got array '
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--> 128 'with shape ' + str(data_shape))
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129 if not check_batch_axis:
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130 data_shape = data_shape[1:]
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ValueError: Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (0, 1)
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### 試したこと
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@@ -71,3 +441,7 @@
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71
441
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まだこれらの分野を勉強し始めたばかりで原因も対処法もよくわからないことだらけなので、回答をいただけたらすぐに試したいと思います。
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よろしくお願いします。
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|
445
|
+
|
|
446
|
+
|
|
447
|
+
実行できているソースなどをすべて記載しました。
|
1
タグの追加
test
CHANGED
|
File without changes
|
test
CHANGED
|
File without changes
|