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012SymClassGConfusion.py
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012SymClassGConfusion.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Dec 14 18:03:18 2023
@author: sebas
"""
from __future__ import print_function
import tensorflow as tf
#tf.enable_eager_execution()
import tensorflow.keras
from tensorflow.keras.models import Sequential,Model
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Subtract,Input,Lambda, Reshape
from tensorflow.keras.layers import Conv2D, MaxPooling2D,Conv1D, MaxPooling1D, LocallyConnected1D, Add
from tensorflow.keras.callbacks import ModelCheckpoint,CSVLogger,ReduceLROnPlateau,EarlyStopping
from tensorflow.keras.constraints import max_norm
from tensorflow.keras import regularizers
import matplotlib.pyplot as plt
import numpy as np
import itertools
import os
np.random.seed(seed=2)
features=3
noise=0.01
l2=0.0001
def probe_function(x):
return x[0]*x[1]+x[2]-2
def probe_function2(x):
return np.sin(x[1]*x[2])-0.5
def target_function(x):
if probe_function(x)*np.random.normal(1,noise)> 0:
return 1
else:
return 0
def target_function2(x):
if probe_function2(x)*np.random.normal(1,noise)> 0:
return 1
else:
return 0
def explicit_gradient(x):
return [1,0,0]
def explicit_gradient2(x):
return [1,0,0]
print('data generation')
x=np.random.rand(10000,features)*2
y1=np.array([target_function(sample) for sample in x])
y2=np.array([target_function2(sample) for sample in x])
x=x[np.where( y1==y2 )[0]]
y=y1[np.where( y1==y2 )[0]]
randomized_order=np.random.permutation(len(x))
x=x[randomized_order]
y=y[randomized_order]
x_train=x[:int(0.8*len(x))]
y_train=y[:int(0.8*len(x))]
x_test=x[int(0.8*len(x)):]
y_test=y[int(0.8*len(x)):]
batch_size = 100
epochs = 10000#1000
input_layer=Input(shape=(x_train.shape[-1],),name='input_layer')
layer_a1=Dense(1000,activation='elu',name='layer_a1',kernel_regularizer=regularizers.L2(l2),bias_regularizer=regularizers.L2(l2))(input_layer) ## does sigmoid give smoother gradients then relu?
layer_a1=Dropout(0.2)(layer_a1)
layer_a2=Dense(1000,activation='elu',name='layer_a2',kernel_regularizer=regularizers.L2(l2),bias_regularizer=regularizers.L2(l2))(layer_a1)
layer_a2=Dropout(0.2)(layer_a2)
output_latent=Dense(1,name='output')(layer_a2)
output=Activation(activation='sigmoid')(output_latent)
model = Model(inputs=input_layer, outputs=output)
model_latent = Model(inputs=input_layer, outputs=output_latent)
model.summary()
# Let's train the model
model.compile(loss='binary_crossentropy',
#optimizer=tensorflow.keras.optimizers.Adadelta(lr=0.0001),
optimizer=tensorflow.keras.optimizers.Adam(),
metrics=['acc'])
reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.5,
patience=200, verbose=1,min_lr=0)
early_stop= EarlyStopping(monitor='loss', patience=400, verbose=1)
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True,
callbacks=[reduce_lr,early_stop])
# Plot training & test loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
#### data analysis
## inverse sigmoid
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def inverse_sigmoid(x):
#return x
return np.log(x/(1-x))
predictions=model.predict(x)
predictions_latent=model_latent.predict(x)
y_analysis = [inverse_sigmoid(prediction) for prediction in predictions]
y_probe = np.array([probe_function(sample) for sample in x])
y_probe2 = np.array([probe_function2(sample) for sample in x])
plt.scatter(predictions_latent,y_probe,label="latent model")
plt.scatter(predictions_latent,y_probe2,label="latent model")
plt.scatter(predictions_latent,x[:,0]*x[:,1]+x[:,2]+np.sin(x[:,1]*x[:,2]),label="latent model")
#plt.scatter(y_analysis,y_probe,label="inverse sigmoid")
# plt.xlim(-5, 5)
# plt.ylim(-0.1, 0.1)
plt.title('prediction correlation with 1st true function')
plt.ylabel('true function')
plt.xlabel('model prediction')
plt.legend(loc='upper left')
plt.show()
# Create scatter plot
fig, ax1 = plt.subplots(figsize=(8, 8),dpi=360)
data_fraction=2000
# Plot dataset 1 on the left y-axis
ax1.scatter(predictions_latent[:data_fraction], y_probe[:data_fraction], label='$x_1*x_2+x_3-2$', alpha=0.4, s=6, c='blue')
ax1.scatter(predictions_latent[:data_fraction], y_probe2[:data_fraction], label='$sin(x_2*x_3)-0.5$', alpha=0.4, s=6, c='green')
ax1.set_xlabel('Latent Model $f$',fontsize=18)
ax1.set_xticklabels([-100,-80,-60,-40,-20,0,20,40],fontsize=18)
ax1.set_ylabel('Symbolic Classification', color='blue',fontsize=18)
ax1.tick_params('y', colors='blue',labelsize=18)
#plt.legend()
# Create a secondary y-axis for dataset 2 on the right
ax2 = ax1.twinx()
ax2.scatter(predictions_latent[:data_fraction], x[:data_fraction,0]*x[:data_fraction,1]+x[:data_fraction,2]+np.sin(x[:data_fraction,1]*x[:data_fraction,2]), label='$x_1*x_2+x_3+sin(x_2*x_3)$', alpha=0.4, s=6, c='red')
ax2.set_ylabel('Neural Network Interpretation', color='red',fontsize=18)
ax2.tick_params('y', colors='red',labelsize=18)
# Set title
#plt.legend(framealpha=0)
plt.title('Correlations Between Neural\nNetwork and Interpretation',fontsize=24)
# Display legend
fig.legend(loc=(0.2,0.65),fontsize=16)
# Display the plot
plt.grid(True)
plt.show()
x_unsure=x[np.where((predictions > 0) & (predictions < 1))[0]]
## neural net gradients
data_set=x_unsure
input_variable = tf.Variable(data_set, dtype=tf.float32)
with tf.GradientTape() as tape:
tape.watch(input_variable)
preds = model_latent(input_variable)
nn_gradients = tape.gradient(preds, input_variable)
## explicit gradients
explicit_gradients = np.array([explicit_gradient(sample) for sample in data_set])
def normalize(x):
norm=np.sqrt(np.sum(x**2))
return x/norm
normalized_nn_gradients=np.array([normalize(sample) for sample in nn_gradients])
normalized_explicit_gradients=np.array([normalize(sample) for sample in explicit_gradients])
dot_products=[np.dot(normalized_explicit_gradients[i],normalized_nn_gradients[i]) for i in range(len(normalized_explicit_gradients))]
MSE=[np.average((normalized_explicit_gradients[i]-normalized_nn_gradients[i])**2) for i in range(len(normalized_explicit_gradients))]
print("average dot produt between grads of nn and true function: ",np.average(dot_products))
print("median dot produt between grads of nn and true function: ",np.median(dot_products))
print("average MSE between grads of nn and true function: ",np.average(MSE))
print("median MSE produt between grads of nn and true function: ",np.median(MSE))
print("length x_unsure", len(x_unsure))
np.savetxt('circle_X.out', data_set, delimiter=',')
np.savetxt('circle_gradients.out', normalized_nn_gradients, delimiter=',')
np.savetxt('X.out', x, delimiter=',')
np.savetxt('Y.out', y, delimiter=',')