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added early stopping, plotting training curves, fixed weights init #31

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added early stopping, plotting training curves, fixed weights init #31

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38 changes: 27 additions & 11 deletions TF_2_x/MNIST-MLP-SELU.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
# Adapted KERAS tutorial

#%%
import tensorflow as tf
import tensorflow.keras as keras
from tensorflow.keras.datasets import mnist
Expand All @@ -8,12 +8,15 @@
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras import backend as K
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


batch_size = 128
num_classes = 10
epochs = 20


# input image dimensions
img_rows, img_cols = 28, 28

Expand Down Expand Up @@ -46,33 +49,46 @@
y_val = y_train[:10000]
y_train = y_train[10000:]


print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_val.shape[0], 'val samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices

# convert class vectors to one-hot vecotrs
y_train = keras.utils.to_categorical(y_train, num_classes)
y_val = keras.utils.to_categorical(y_val, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Flatten())
model.add(Dense(512, activation='selu',kernel_initializer='lecun_normal',bias_initializer='zeros'))
model.add(AlphaDropout(0.05))
model.add(Dense(256, activation='selu',kernel_initializer='lecun_normal',bias_initializer='zeros'))
model.add(AlphaDropout(0.05))
model.add(Dense(num_classes, activation='softmax',kernel_initializer='lecun_normal',bias_initializer='zeros'))
model = Sequential([
Flatten(input_shape = (28,28)),
Dense(512, activation='selu',kernel_initializer='lecun_normal',bias_initializer='zeros'),
AlphaDropout(0.05),
Dense(256, activation='selu',kernel_initializer='lecun_normal',bias_initializer='zeros'),
AlphaDropout(0.05),
Dense(num_classes, activation='softmax',kernel_initializer='glorot_normal') #best practice to use glorot with softmax
])


model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(learning_rate=0.001),
metrics=['accuracy'])

model.fit(x_train, y_train,
#use early stopping callbacks
early_stopping_cb = tf.keras.callbacks.EarlyStopping(monitor = "val_loss", patience = 6)

history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_val, y_val))
validation_data=(x_val, y_val),
callbacks = [early_stopping_cb]
)

#visualize training curves
pd.DataFrame(history.history).plot(figsize=(8,5))
plt.show()

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
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