import numpy as np import matplotlib.pyplot as plt from keras.datasets import mnist from keras.layers import Activation, Dense from keras.models import Sequential, load_model from keras.utils.np_utils import to_categorical (X_train, y_train), (X_test, y_test) = mnist.load_data() X_train = X_train.reshape(X_train.shape[0], 784)[:6000] X_test = X_test.reshape(X_test.shape[0], 784)[:1000] y_train = to_categorical(y_train)[:6000] y_test = to_categorical(y_test)[:1000] model = Sequential() model.add(Dense(256, input_dim=784)) model.add(Activation("sigmoid")) model.add(Dense(128)) model.add(Activation("sigmoid")) model.add(Dense(10)) model.add(Activation("softmax")) model.compile(optimizer="sgd", loss="categorical_crossentropy", metrics=["accuracy"]) model.fit(X_train, y_train, verbose=True) score = model.evaluate(X_test, y_test, verbose=False) print("evaluate loss: {0[0]}\nevaluate acc: {0[1]}".format(score)) # テストデータの最初の10枚を表示 for i in range(10): plt.subplot(1, 10, i+1) plt.imshow(X_test[i].reshape((28,28)), "gray") plt.show() pred = np.argmax(model.predict(X_test[0:10]), axis=1) print(pred) #[7 2 1 0 6 1 7 0 6 7]
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