Jupyter Notebookなどで、コードを実装して実際に確かめてみましょう。
keras、TensorFlowなどのインストールに関してはこのページを参照
Jetson Nanoを使う場合、コードにあるとおりにデータセットを使うとメモリー不足で落ちます（train_test_splitでデータ分割するところ）。SWAP領域を４GB程度は確保しておきましょう。
お急ぎの向きはJetsonではなくGoogleのColaboratoryを使うほうがいいです。
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import numpy as np import matplotlib.pyplot as plt from keras.datasets import imdb from keras.preprocessing.text import Tokenizer from keras import models from keras import layers from keras.callbacks import EarlyStopping from sklearn.model_selection import train_test_split #データの準備 (train_data,train_labels),(test_data,test_labels) = imdb.load_data(num_words=10000) train_data.shape train_data[0] len(train_data[0]) train_labels[0] #前処理 tokenizer = Tokenizer(num_words=10000) x_train = tokenizer.sequences_to_matrix(train_data,mode='binary') x_test = tokenizer.sequences_to_matrix(test_data,mode='binary') x_train.shape y_train = np.asarray(train_labels).astype('float32') y_test = np.asarray(test_labels).astype('float32') x_train,x_val,y_train,y_val = train_test_split(x_train,y_train,test_size=0.1,random_state=0) print(x_train.shape,x_val.shape) #モデル構築 model = models.Sequential() model.add(layers.Dense(32,activation='relu',input_shape=(10000,))) model.add(layers.Dense(16,activation='relu')) model.add(layers.Dense(1,activation='sigmoid')) model.summary() model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) #学習実行 history = model.fit(x=x_train, y=y_train, epochs=20, batch_size=512, verbose=1, validation_data=(x_val,y_val)) #結果の可視化 history_dict = history.history history_dict.keys() #キーに注意,accだったりaccuracyだったりします acc = history_dict['accuracy'] val_acc = history_dict['val_accuracy'] epochs = range(1,len(acc) + 1) plt.figure(figsize=(12,8)) plt.plot(epochs,acc,label='acc') plt.plot(epochs,val_acc,label='val_acc') plt.ylim((0,1)) plt.legend(loc='best') plt.show() loss = history_dict['loss'] val_loss = history_dict['val_loss'] epochs = range(1,len(loss) + 1) plt.figure(figsize=(12,8)) plt.plot(epochs,loss,label='loss') plt.plot(epochs,val_loss,label='val_loss') plt.ylim((0,1)) plt.legend(loc='best') plt.show() #ほどよいEpoch数を教えてもらう callbacks = [EarlyStopping(monitor='val_accuracy',patience=3)] history = model.fit(x=x_train, y=y_train, epochs=20, batch_size=512, verbose=1, callbacks=callbacks, validation_data=(x_val,y_val)) # score = model.evaluate(x_test,y_test) print("Test set loss:{},Test set accuray:{}".format(score[0],score[1])) #モデルの再構築と実行 -> ロジスティック回帰の形 model = models.Sequential() model.add(layers.Dense(1,activation='sigmoid',input_shape=(10000,))) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) history = model.fit(x=x_train, y=y_train, epochs=100, batch_size=512, verbose=1, callbacks=callbacks, validation_data=(x_val,y_val)) score = model.evaluate(x_test,y_test) print("Test set loss:{},Test set accuray:{}".format(score[0],score[1])) |
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