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import pandas as pd
import numpy as np
%matplotlib inline
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output1 = np.load("lstm_seq30/output.npy")
output1_df = pd.DataFrame(output1, columns=["output (seq: 30)"], index=range(30, 30 + len(output1)))
losses1_df = pd.DataFrame(np.load("lstm_seq30/losses.npy"), columns=["epoch", "loss (seq: 30)"])
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output2 = np.load("lstm_seq40/output.npy")
output2_df = pd.DataFrame(output2, columns=["output (seq: 40)"], index=range(40, 40 + len(output2)))
losses2_df = pd.DataFrame(np.load("lstm_seq40/losses.npy"), columns=["epoch", "loss (seq: 40)"])
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output3 = np.load("lstm_basic/output.npy")
output3_df = pd.DataFrame(output3, columns=["output (seq: 50)"], index=range(50, 50 + len(output3)))
losses3_df = pd.DataFrame(np.load("lstm_basic/losses.npy"), columns=["epoch", "loss (seq: 50)"])
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output4 = np.load("lstm_seq60/output.npy")
output4_df = pd.DataFrame(output4, columns=["output (seq: 60)"], index=range(60, 60 + len(output4)))
losses4_df = pd.DataFrame(np.load("lstm_seq60/losses.npy"), columns=["epoch", "loss (seq: 60)"])
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output5 = np.load("lstm_seq70/output.npy")
output5_df = pd.DataFrame(output5, columns=["output (seq: 70)"], index=range(70, 70 + len(output5)))
losses5_df = pd.DataFrame(np.load("lstm_seq70/losses.npy"), columns=["epoch", "loss (seq: 70)"])
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train = np.load("train_data/normal.npy")
train_df = pd.DataFrame(train[:, 0], columns=["train"])
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merged = pd.concat([train_df, output1_df, output2_df, output3_df, output4_df, output5_df])
merged.plot(figsize=(15, 5), grid=True, style=["-", "-", "-", "-", "-", "k--"])
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In [9]:
merged = pd.merge(losses1_df, losses2_df)
merged = pd.merge(merged, losses3_df)
merged = pd.merge(merged, losses4_df)
merged = pd.merge(merged, losses5_df)
merged.plot(figsize=(15, 5), grid=True, logy=True, x="epoch")
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