質問編集履歴
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該当ソースコードのところに全体のコードを追加しました。
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@@ -2,6 +2,20 @@
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convLSTMを用いた画像予測を行っています。
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%matplotlib inline
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fig, axes = plt.subplots(1, 2, figsize=(12,6))
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axes[0].imshow((y_test[i]+1)/2)
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axes[1].imshow((model.predict(x_test[[i]]).reshape(100,180,3)+1)/2)
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i=0のところを０以外の数値にするとエラーが出ます。
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```ここに言語名を入力
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sns
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from sklearn.model_selection import train_test_split
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import glob
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from PIL import Image
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from tqdm import tqdm
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import zipfile
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import io
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# 縮小後の画像サイズ
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height = 100
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width = 180
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# 読み込んだ画像を入れる配列
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imgs=np.empty((0, height, width, 3))
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# zipファイルを読み込んでnumpy配列にする
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zip_f = zipfile.ZipFile('drive/My Drive/Colab Notebooks/convLSTM/wide.zip')
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for name in tqdm(zip_f.namelist()):
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with zip_f.open(name) as file:
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path = io.BytesIO(file.read()) #解凍
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img = Image.open(path)
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img = img.resize((width, height))
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img_np = np.array(img).reshape(1, height, width, 3)
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imgs = np.append(imgs, img_np, axis=0)
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# 時系列で学習できる形式に整える
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n_seq = 5
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n_sample = imgs.shape[0] - n_seq
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x = np.zeros((n_sample, n_seq, height, width, 3))
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y = np.zeros((n_sample, height, width, 3))
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for i in range(n_sample):
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x[i] = imgs[i:i+n_seq]
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y[i] = imgs[i+n_seq]
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x, y = (x-128)/128, (y-128)/128
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.1, shuffle = False)
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from keras import layers
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from keras.layers.core import Activation
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from tensorflow.keras.models import Model
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inputs = layers.Input(shape=(5, height, width, 3))
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x0 = layers.ConvLSTM2D(filters=16, kernel_size=(3,3), padding="same", return_sequences=True, data_format="channels_last")(inputs)
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x0 = layers.BatchNormalization(momentum=0.6)(x0)
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x0 = layers.ConvLSTM2D(filters=16, kernel_size=(3,3), padding="same", return_sequences=True, data_format="channels_last")(x0)
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x0 = layers.BatchNormalization(momentum=0.8)(x0)
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x0 = layers.ConvLSTM2D(filters=3, kernel_size=(3,3), padding="same", return_sequences=False, data_format="channels_last")(x0)
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out = Activation('tanh')(x0)
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model = Model(inputs=inputs, outputs=out)
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model.summary()
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model.compile(optimizer='rmsprop',
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loss='mae', metrics=['mse'])
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call_backs=[EarlyStopping(monitor="val_loss",patience=5)]
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model.fit(x_train, y_train, batch_size=16, epochs=100, verbose=2, validation_split=0.2, shuffle=True, callbacks=call_backs)
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# 描画
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%matplotlib inline
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fig, axes = plt.subplots(1, 2, figsize=(12,6))
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axes[1].imshow((model.predict(x_test[[i]]).reshape(100,180,3)+1)/2)
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```
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