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keras_early_stop.py
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keras_early_stop.py
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# coding: utf-8
# In[13]:
from ds_utils.imports import *
# In[14]:
(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()
# see https://github.com/yang-zhang/code-data-science/blob/master/numpy_newaxis.ipynb
X_train = X_train[:, np.newaxis]
X_test = X_test[:, np.newaxis]
y_train = keras.utils.np_utils.to_categorical(y_train, 10)
y_test = keras.utils.np_utils.to_categorical(y_test, 10)
# In[15]:
def make_compile_model():
model = keras.models.Sequential([
keras.layers.Convolution2D(
filters=32,
kernel_size=(3, 3),
activation='relu',
input_shape=(1, 28, 28)), keras.layers.Convolution2D(
filters=32, kernel_size=(3, 3), activation='relu'),
keras.layers.MaxPooling2D(pool_size=(2, 2)),
keras.layers.Dropout(0.25), keras.layers.Flatten(), keras.layers.Dense(
128, activation='relu'), keras.layers.Dropout(0.5),
keras.layers.Dense(
10, activation='softmax')
])
model.compile(
optimizer=keras.optimizers.Adam(lr=0.001),
loss=keras.losses.categorical_crossentropy,
metrics=['accuracy'])
return model
# In[16]:
def sample_xy(x, y, sample_size):
sample = np.random.choice(range(x.shape[0]), sample_size)
return x[sample], y[sample]
# In[17]:
X_train_sample, y_train_sample = sample_xy(X_train, y_train, 100)
X_test_sample, y_test_sample = sample_xy(X_test, y_test, 500)
# In[18]:
model = make_compile_model()
model.fit(X_train_sample,
y_train_sample,
validation_data=[X_test_sample, y_test_sample],
epochs=1000)
# In[19]:
model = make_compile_model()
early_stopping = keras.callbacks.EarlyStopping(
monitor='val_loss', min_delta=0, patience=100)
model.fit(X_train_sample,
y_train_sample,
validation_data=[X_test_sample, y_test_sample],
epochs=1000,
callbacks=[early_stopping])
# ## References
# - https://keras.io/callbacks/#earlystopping/
# In[ ]: