mirror of
https://github.com/Findus23/collision-analyisis-and-interpolation.git
synced 2024-09-19 15:13:50 +02:00
90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
import os
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import random
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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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from matplotlib import pyplot as plt
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from CustomScaler import CustomScaler
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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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[[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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Y = np.array([s.water_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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[[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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if os.path.exists("model.hd5"):
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model = load_model("model.hd5")
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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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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("model.hd5")
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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 = 1e23
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wpcode = 15 / 100
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wtcode = 15 / 100
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gammacode = 0.7
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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_data(testinput)
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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, (100, 100))
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plt.pcolormesh(xgrid, ygrid, outgrid, cmap="Blues", vmin=0, vmax=1)
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plt.colorbar()
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plt.savefig("keras.png", transparent=True)
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plt.show()
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