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Update keras_example.py #95

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74 changes: 26 additions & 48 deletions cnn_class/keras_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,14 +19,8 @@

from benchmark import get_data, error_rate


# helper
# def y2indicator(Y):
# N = len(Y)
# K = len(set(Y))
# I = np.zeros((N, K))
# I[np.arange(N), Y] = 1
# return I
# get the data
train, test = get_data()

def rearrange(X):
# input is (32, 32, 3, N)
Expand All @@ -39,10 +33,6 @@ def rearrange(X):
# return out / 255
return (X.transpose(3, 0, 1, 2) / 255.).astype(np.float32)


# get the data
train, test = get_data()

# Need to scale! don't leave as 0..255
# Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1)
# So flatten it and make it 0..9
Expand All @@ -55,49 +45,39 @@ def rearrange(X):
Ytest = test['y'].flatten() - 1
del test



# get shapes
K = len(set(Ytrain))



# make the CNN
i = Input(shape=Xtrain.shape[1:])
x = Conv2D(filters=20, kernel_size=(5, 5))(i)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)

x = Conv2D(filters=50, kernel_size=(5, 5))(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)

x = Flatten()(x)
x = Dense(units=500)(x)
x = Activation('relu')(x)
x = Dropout(0.3)(x)
x = Dense(units=K)(x)
x = Activation('softmax')(x)

model = Model(inputs=i, outputs=x)

model = Sequential([
Input(shape=Xtrain.shape[1:]),
Conv2D(filters=20, kernel_size=(5, 5)), # First Conv layer
BatchNormalization(),
Activation('relu'),
MaxPooling2D(),

Conv2D(filters=50, kernel_size=(5, 5)), # Second Conv layer
BatchNormalization(),
Activation('relu'),
MaxPooling2D(),

Flatten(),
Dense(units=500), # Fully connected layer
Activation('relu'),
Dropout(0.3),
Dense(units=K), # Output layer
Activation('softmax')
])

# list of losses: https://keras.io/losses/
# list of optimizers: https://keras.io/optimizers/
# list of metrics: https://keras.io/metrics/
model.compile(
loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy']
)

# note: multiple ways to choose a backend
# either theano, tensorflow, or cntk
# https://keras.io/backend/


# gives us back a <keras.callbacks.History object at 0x112e61a90>
r = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=10, batch_size=32)
print("Returned:", r)
Expand All @@ -113,9 +93,7 @@ def rearrange(X):
plt.show()

# accuracies
plt.plot(r.history['accuracy'], label='acc')
plt.plot(r.history['val_accuracy'], label='val_acc')
plt.plot(r.history['accuracy'], label='accuracy')
plt.plot(r.history['val_accuracy'], label='val_accuracy')
plt.legend()
plt.show()