In [1]:
import pandas as pd
import numpy as np
%matplotlib inline
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initial = np.load("lstm_basic/initial.npy")
initial_df = pd.DataFrame(initial, columns=["initial"])
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output1 = np.load("lstm_basic/output.npy")
output1_df = pd.DataFrame(output1, columns=["output (optimizer: GD)"], index=range(len(initial), len(initial) + len(output1)))
losses1_df = pd.DataFrame(np.load("lstm_basic/losses.npy"), columns=["epoch", "loss (optimizer: GD)"])
In [4]:
output2 = np.load("lstm_adam/output.npy")
output2_df = pd.DataFrame(output2, columns=["output (optimizer: Adam)"], index=range(len(initial), len(initial) + len(output2)))
losses2_df = pd.DataFrame(np.load("lstm_adam/losses.npy"), columns=["epoch", "loss (optimizer: Adam)"])
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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, initial_df, output1_df, output2_df])
merged.plot(figsize=(15, 5), grid=True, style=["-", "-", "-", "k--"])
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In [7]:
merged = pd.merge(losses1_df, losses2_df)
merged.plot(figsize=(15, 5), grid=True, logy=True, x="epoch")
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