質問編集履歴
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環境は macOS version:10.14.5でjupyter labを使っています。
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環境は macOS version:10.14.5、python3.7でjupyter labを使っています。
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`plt.plot`
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`plt.plot([1, 5], [1, 10])`を書いてみたのですが、グラフが表示されませんでした。
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pythonやkerasのバージョンが原因で表示されないことはあるのでしょうか？
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```Python
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print(epochs)
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plt.plot([1, 5], [1, 10]) # 追記
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plt.plot(epochs, acc, label="acc", ls="-", marker="o")
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何かわかることがございましたら、ご教示いただければ幸いです。
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---
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**追記**
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`plt.plot`の前に、`print`文を挿入してみたのですが、出力は上と変わらずでした。どうやら`model.fit`の行で処理が終わっているようです。
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さらに、別セルで`print(history)`で確認したのですが、"__NameError: name 'history' is not defined__" となって`model.fit`が`history`に代入されていませんでした。
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```Python
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import numpy as np
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import matplotlib.pyplot as plt
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from keras.datasets import mnist
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from keras.layers import Activation, Dense, Dropout
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from keras.models import Sequential, load_model
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from keras.utils.np_utils import to_categorical
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from keras import optimizers
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from keras import models
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from keras import layers
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import tensorflow
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import tensorboard
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from livelossplot import PlotLossesKeras
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%matplotlib inline
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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X_train = X_train.reshape(X_train.shape[0], 784)[:6000]
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X_test = X_test.reshape(X_test.shape[0], 784)[:1000]
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y_train = to_categorical(y_train)[:6000]
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y_test = to_categorical(y_test)[:1000]
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model = Sequential()
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model.add(Dense(256, input_dim=784))
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model.add(Activation("sigmoid"))
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model.add(Dense(128))
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model.add(Activation("sigmoid"))
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model.add(Dropout(rate=0.5))
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model.add(Dense(10))
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model.add(Activation("softmax"))
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sgd = optimizers.SGD(lr=0.1)
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model.compile(optimizer=sgd, loss="categorical_crossentropy", metrics=["accuracy"])
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# callbacks = [PlotLossesKeras()]
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history = model.fit(
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y_train,
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batch_size=32,
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epochs=5,
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verbose=1,
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validation_data=(X_test, y_test),
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# callbacks=[PlotLossesKeras()]
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)
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acc = history.history["acc"]
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val_acc = history.history["val_acc"]
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loss = history.history["loss"]
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val_loss = history.history["val_loss"]
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epochs = range(1, len(acc) + 1)
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print("-" * 10)
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print(epochs)
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print(history)
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print("-" * 10)
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plt.plot(epochs, acc, label="acc", ls="-", marker="o")
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plt.plot(epochs, val_acc, label="val_acc", ls="-", marker="x")
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plt.ylabel("accuracy")
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plt.xlabel("epoch")
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plt.legend(loc="best")
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```
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