mirror of
https://github.com/Findus23/collision-analyisis-and-interpolation.git
synced 2024-09-19 15:13:50 +02:00
add pytorch model
This commit is contained in:
parent
88bbf9a160
commit
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5 changed files with 227 additions and 131 deletions
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@ -1,4 +1,4 @@
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from typing import List, Iterator
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from typing import List, Iterator, Optional
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import numpy as np
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@ -10,8 +10,8 @@ class CustomScaler:
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"""
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def __init__(self):
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self.means = None
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self.stds = None
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self.means: Optional[np.ndarray] = None
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self.stds: Optional[np.ndarray] = None
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def fit(self, data: np.ndarray) -> None:
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self.means = np.mean(data, 0)
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19
network.py
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network.py
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from torch import nn
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class Network(nn.Module):
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def __init__(self):
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super().__init__()
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self.hidden = nn.Linear(6, 50)
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self.output = nn.Linear(50, 4)
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self.sigmoid = nn.Sigmoid()
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self.relu = nn.ReLU()
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def forward(self, x):
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x = self.hidden(x)
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x = self.relu(x)
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x = self.output(x)
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x = self.sigmoid(x)
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return x
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@ -1,101 +1,167 @@
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import os
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import json
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import random
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from pathlib import Path
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import keras
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import numpy as np
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from keras import Sequential
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from keras.engine.saving import load_model
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from keras.layers import Dense
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from keras.utils import plot_model
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import torch
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from matplotlib import pyplot as plt
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from matplotlib.axes import Axes
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from matplotlib.figure import Figure
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from matplotlib.lines import Line2D
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from torch import nn, optim, from_numpy, Tensor
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from torch.utils.data import DataLoader, TensorDataset
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from CustomScaler import CustomScaler
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from config import water_fraction
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from network import Network
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from simulation_list import SimulationList
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simulations = SimulationList.jsonlines_load()
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train_data = set([s for s in simulations.simlist])
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new_data = [s for s in simulations.simlist if s.type != "original"]
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random.seed(1)
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test_data = set(random.sample(new_data, int(len(new_data) * 0.2)))
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train_data -= test_data
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print(len(train_data), len(test_data))
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X = np.array(
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def train():
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filename = "rsmc_dataset"
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simulations = SimulationList.jsonlines_load(Path(f"{filename}.jsonl"))
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# random.seed(1)
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test_data = random.sample(simulations.simlist, int(len(simulations.simlist) * 0.2))
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test_set = set(test_data) # use a set for faster *in* computation
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train_data = [s for s in simulations.simlist if s not in test_set]
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print(len(train_data), len(test_data))
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X = np.array(
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[[s.alpha, s.v, s.projectile_mass, s.gamma, s.target_water_fraction, s.projectile_water_fraction] for s in
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train_data])
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scaler = CustomScaler()
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scaler.fit(X)
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x = scaler.transform_data(X)
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print(x.shape)
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if water_fraction:
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Y = np.array([s.water_retention_both for s in train_data])
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else:
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Y = np.array([s.mass_retention_both for s in train_data])
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print(Y.shape)
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X_test = np.array(
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scaler = CustomScaler()
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scaler.fit(X)
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x = scaler.transform_data(X)
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print(x.shape)
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Y = np.array([[
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s.water_retention_both, s.mantle_retention_both, s.core_retention_both,
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s.output_mass_fraction
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] for s in train_data])
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X_test = np.array(
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[[s.alpha, s.v, s.projectile_mass, s.gamma, s.target_water_fraction, s.projectile_water_fraction] for s in
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test_data])
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Y_test = np.array([s.mass_retention_both for s in test_data])
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x_test = scaler.transform_data(X_test)
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# print(X_test)
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# print(X[0])
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# exit()
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random.seed()
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tbCallBack = keras.callbacks.TensorBoard(log_dir='./logs/{}'.format(random.randint(0, 100)), histogram_freq=1,
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batch_size=32, write_graph=True,
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write_grads=True, write_images=True, embeddings_freq=0,
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embeddings_layer_names=None, embeddings_metadata=None, embeddings_data=None,
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update_freq='epoch')
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modelname = "model.hd5" if water_fraction else "model_mass.hd5"
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Y_test = np.array([[
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s.water_retention_both, s.mantle_retention_both, s.core_retention_both,
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s.output_mass_fraction
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] for s in test_data])
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x_test = scaler.transform_data(X_test)
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random.seed()
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if os.path.exists(modelname):
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model = load_model(modelname)
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else:
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model = Sequential()
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model.add(Dense(6, input_dim=6, activation='relu'))
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model.add(Dense(4, kernel_initializer='normal', activation='relu'))
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model.add(Dense(3, kernel_initializer='normal', activation='relu'))
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model.add(Dense(1, kernel_initializer='normal', activation="sigmoid"))
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model.compile(loss='mean_squared_error', optimizer='adam')
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dataset = TensorDataset(from_numpy(x).to(torch.float), from_numpy(Y).to(torch.float))
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train_dataset = TensorDataset(from_numpy(x_test).to(torch.float), from_numpy(Y_test).to(torch.float))
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dataloader = DataLoader(dataset, batch_size=64, shuffle=True)
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validation_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=False)
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model.summary()
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plot_model(model, "model.png", show_shapes=True, show_layer_names=True)
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model.fit(x, Y, epochs=200, callbacks=[tbCallBack], validation_data=(x_test, Y_test))
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loss = model.evaluate(x_test, Y_test)
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print(loss)
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if loss > 0.04:
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# exit()
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...
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# print("-------------------------------------")
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# exit()
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model.save(modelname)
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network = Network()
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xrange = np.linspace(-0.5, 60.5, 300)
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yrange = np.linspace(0.5, 5.5, 300)
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xgrid, ygrid = np.meshgrid(xrange, yrange)
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mcode = 1e24
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wpcode = 15 / 100
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wtcode = 15 / 100
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gammacode = 0.6
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testinput = np.array([[np.nan, np.nan, mcode, gammacode, wtcode, wpcode]] * 300 * 300)
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testinput[::, 0] = xgrid.flatten()
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testinput[::, 1] = ygrid.flatten()
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testinput = scaler.transform_data(testinput)
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loss_fn = nn.MSELoss()
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print(testinput)
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print(testinput.shape)
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testoutput = model.predict(testinput)
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outgrid = np.reshape(testoutput, (300, 300))
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print("minmax")
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print(np.nanmin(outgrid), np.nanmax(outgrid))
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cmap = "Blues" if water_fraction else "Oranges"
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plt.imshow(outgrid, interpolation='none', cmap=cmap, aspect="auto", origin="lower", vmin=0, vmax=1,
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optimizer = optim.Adam(network.parameters())
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loss_train = []
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loss_vali = []
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max_epochs = 120
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epochs = 0
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fig: Figure = plt.figure()
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ax: Axes = fig.gca()
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x_axis = np.arange(epochs)
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loss_plot: Line2D = ax.plot(x_axis, loss_train, label="loss_train")[0]
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vali_plot: Line2D = ax.plot(x_axis, loss_vali, label="loss_validation")[0]
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ax.legend()
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plt.ion()
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plt.pause(0.01)
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plt.show()
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for e in range(max_epochs):
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print(f"Epoch: {e}")
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total_loss = 0
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network.train()
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for xs, ys in dataloader:
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# Training pass
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optimizer.zero_grad()
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output = network(xs)
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loss = loss_fn(output, ys)
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loss.backward()
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optimizer.step()
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total_loss += loss.item()
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loss_train.append(float(total_loss / len(dataloader)))
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print(f"Training loss: {total_loss / len(dataloader)}")
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# validation:
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network.eval()
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total_loss_val = 0
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for xs, ys in validation_dataloader:
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output = network(xs)
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total_loss_val += loss_fn(output, ys).item()
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loss_vali.append(float(total_loss_val / len(validation_dataloader)))
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print(f"Validation loss: {total_loss_val / len(validation_dataloader)}")
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epochs += 1
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x_axis = np.arange(epochs)
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loss_plot.set_xdata(x_axis)
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vali_plot.set_xdata(x_axis)
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loss_plot.set_ydata(loss_train)
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vali_plot.set_ydata(loss_vali)
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ax.relim()
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ax.autoscale_view(True, True, True)
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plt.pause(0.01)
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# plt.draw()
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# if epochs > 6:
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# a = np.sum(np.array(loss_vali[-3:]))
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# b = np.sum(np.array(loss_vali[-6:-3]))
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# if a > b: # overfitting on training data, stop training
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# print("early stopping")
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# break
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plt.ioff()
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torch.save(network.state_dict(), "pytorch_model.zip")
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with open("pytorch_model.json", "w") as f:
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export_dict = {}
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value_tensor: Tensor
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for key, value_tensor in network.state_dict().items():
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export_dict[key] = value_tensor.detach().tolist()
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export_dict["means"] = scaler.means.tolist()
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export_dict["stds"] = scaler.stds.tolist()
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json.dump(export_dict, f)
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xrange = np.linspace(-0.5, 60.5, 300)
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yrange = np.linspace(0.5, 5.5, 300)
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xgrid, ygrid = np.meshgrid(xrange, yrange)
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mcode = 1e24
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wpcode = 1e-4
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wtcode = 1e-4
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gammacode = 0.6
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testinput = np.array([[np.nan, np.nan, mcode, gammacode, wtcode, wpcode]] * 300 * 300)
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testinput[::, 0] = xgrid.flatten()
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testinput[::, 1] = ygrid.flatten()
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testinput = scaler.transform_data(testinput)
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print(testinput)
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print(testinput.shape)
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testoutput: Tensor = network(from_numpy(testinput).to(torch.float))
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data = testoutput.detach().numpy()
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outgrid = np.reshape(data[::, 0], (300, 300))
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print("minmax")
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print(np.nanmin(outgrid), np.nanmax(outgrid))
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cmap = "Blues"
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plt.title(
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"m={:3.0e}, gamma={:3.1f}, wt={:2.0f}%, wp={:2.0f}%\n".format(mcode, gammacode, wtcode * 100, wpcode * 100))
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plt.imshow(outgrid, interpolation='none', cmap=cmap, aspect="auto", origin="lower", vmin=0, vmax=1,
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extent=[xgrid.min(), xgrid.max(), ygrid.min(), ygrid.max()])
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plt.colorbar().set_label("water retention fraction" if water_fraction else "core mass retention fraction")
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plt.xlabel("impact angle $\\alpha$ [$^{\circ}$]")
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plt.ylabel("velocity $v$ [$v_{esc}$]")
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plt.tight_layout()
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plt.savefig("../arbeit/images/plots/nn2.pdf")
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plt.show()
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plt.colorbar().set_label("water retention fraction")
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plt.xlabel("impact angle $\\alpha$ [$^{\circ}$]")
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plt.ylabel("velocity $v$ [$v_{esc}$]")
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plt.tight_layout()
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# plt.savefig("/home/lukas/tmp/nn.svg", transparent=True)
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plt.show()
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if __name__ == '__main__':
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train()
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import json
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from typing import Optional
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class Simulation:
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def second_largest_aggregate_mantle_fraction(self) -> float:
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return 1 - self.second_largest_aggregate_core_fraction - self.second_largest_aggregate_water_fraction
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@property
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def projectile_mantle_fraction(self):
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return 1 - self.projectile_water_fraction - self.projectile_core_fraction
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"""
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return self.largest_aggregate_mass * self.largest_aggregate_water_fraction / self.initial_water_mass
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@property
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def output_mass_fraction(self) -> Optional[float]:
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if not self.largest_aggregate_mass:
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return 0 # FIXME
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return self.second_largest_aggregate_mass / self.largest_aggregate_mass
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@property
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def original_simulation(self) -> bool:
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return self.type == "original"
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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from keras.engine.saving import load_model
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import torch
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from matplotlib.collections import QuadMesh
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from matplotlib.widgets import Slider
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from sklearn.preprocessing import StandardScaler
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from CustomScaler import CustomScaler
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from network import Network
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from simulation_list import SimulationList
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simlist = SimulationList.jsonlines_load()
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simlist = SimulationList.jsonlines_load(Path("rsmc_dataset.jsonl"))
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resolution = 100
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X = simlist.X
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scaler = StandardScaler()
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scaler.fit(X)
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data = simlist.X
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scaler = CustomScaler()
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scaler.fit(data)
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fig, ax = plt.subplots()
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plt.subplots_adjust(bottom=0.35)
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t = np.arange(0.0, 1.0, 0.001)
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mcode_default, gamma_default, wt_default, wp_default = [24.0, 1, 15.0, 15.0]
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xrange = np.linspace(-0.5, 60.5, 100)
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yrange = np.linspace(0.5, 5.5, 100)
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xgrid, ygrid = np.meshgrid(xrange, yrange)
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mcode = 24.
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wpcode = 15 / 100
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wtcode = 15 / 100
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gammacode = 1
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alpharange = np.linspace(-0.5, 60.5, resolution)
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vrange = np.linspace(0.5, 5.5, resolution)
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grid_alpha, grid_v = np.meshgrid(alpharange, vrange)
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testinput = np.array([[np.nan, np.nan, mcode, gammacode, wtcode, wpcode]] * 100 * 100)
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testinput[::, 0] = xgrid.flatten()
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testinput[::, 1] = ygrid.flatten()
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testinput = scaler.transform(testinput)
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model = Network()
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model.load_state_dict(torch.load("pytorch_model.zip"))
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model = load_model("model.hd5")
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datagrid = np.zeros_like(grid_alpha)
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testoutput = model.predict(testinput)
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outgrid = np.reshape(testoutput, (100, 100))
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mesh = plt.pcolormesh(xgrid, ygrid, outgrid, cmap="Blues", vmin=0, vmax=1) # type:QuadMesh
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mesh = plt.pcolormesh(grid_alpha, grid_v, datagrid, cmap="Blues", vmin=0, vmax=1, shading="auto") # type:QuadMesh
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plt.colorbar()
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axcolor = 'lightgoldenrodyellow'
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ax_mcode = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)
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ax_gamma = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)
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ax_wt = plt.axes([0.25, 0.20, 0.65, 0.03], facecolor=axcolor)
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ax_wp = plt.axes([0.25, 0.25, 0.65, 0.03], facecolor=axcolor)
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ax_mcode = plt.axes([0.25, 0.1, 0.65, 0.03])
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ax_gamma = plt.axes([0.25, 0.15, 0.65, 0.03])
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ax_wt = plt.axes([0.25, 0.20, 0.65, 0.03])
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ax_wp = plt.axes([0.25, 0.25, 0.65, 0.03])
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ax_mode = plt.axes([0.25, 0.05, 0.65, 0.03])
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s_mcode = Slider(ax_mcode, 'mcode', 21, 25, valinit=mcode_default)
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s_gamma = Slider(ax_gamma, 'gamma', 0.1, 1, valinit=gamma_default)
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s_wt = Slider(ax_wt, 'wt', 10, 20, valinit=wt_default)
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s_wp = Slider(ax_wp, 'wp', 10, 20, valinit=wp_default)
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s_wt = Slider(ax_wt, 'wt', 1e-5, 1e-3, valinit=wt_default)
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s_wp = Slider(ax_wp, 'wp', 1e-5, 1e-3, valinit=wp_default)
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s_mode = Slider(ax_mode, 'shell/mantle/core/mass_fraction', 1, 4, valinit=1, valstep=1)
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def update(val):
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mcode = 10 ** s_mcode.val
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mcode = s_mcode.val
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gamma = s_gamma.val
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wt = s_wt.val / 100
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wp = s_wp.val / 100
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testinput = np.array([[np.nan, np.nan, mcode, gamma, wt, wp]] * 100 * 100)
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testinput[::, 0] = xgrid.flatten()
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testinput[::, 1] = ygrid.flatten()
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testinput = scaler.transform(testinput)
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wt = s_wt.val
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wp = s_wp.val
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mode = s_mode.val
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testinput = np.array([[np.nan, np.nan, 10 ** mcode, gamma, wt, wp]] * resolution * resolution)
|
||||
testinput[::, 0] = grid_alpha.flatten()
|
||||
testinput[::, 1] = grid_v.flatten()
|
||||
testinput = scaler.transform_data(testinput)
|
||||
|
||||
testoutput = model.predict(testinput)
|
||||
outgrid = np.reshape(testoutput, (100, 100))
|
||||
# if not isinstance(datagrid, np.ndarray):
|
||||
# return False
|
||||
formatedgrid = outgrid[:-1, :-1]
|
||||
try:
|
||||
testoutput: torch.Tensor = model(torch.from_numpy(testinput).to(torch.float))
|
||||
data = testoutput.detach().numpy()
|
||||
print(data.shape)
|
||||
except TypeError: # can't convert np.ndarray of type numpy.object_.
|
||||
data = np.zeros((resolution ** 2, 3))
|
||||
|
||||
mesh.set_array(formatedgrid.ravel())
|
||||
datagrid = np.reshape(data[::, mode - 1], (resolution, resolution))
|
||||
|
||||
mesh.set_array(datagrid.ravel())
|
||||
|
||||
fig.canvas.draw_idle()
|
||||
|
||||
|
||||
update(None)
|
||||
|
||||
s_gamma.on_changed(update)
|
||||
s_mcode.on_changed(update)
|
||||
s_wp.on_changed(update)
|
||||
s_wt.on_changed(update)
|
||||
s_mode.on_changed(update)
|
||||
|
||||
plt.show()
|
||||
|
|
Loading…
Reference in a new issue