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number_parameters_dense_conv_nn.py
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number_parameters_dense_conv_nn.py
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# coding: utf-8
# https://medium.com/p/3eb35ab7f3c/edit
# In[41]:
import utilities
# In[1]:
img_rows, img_cols = 224, 224
colors = 3
input_size = img_rows * img_cols * colors
input_shape = (img_rows, img_cols, colors)
num_classes = 10
# In[2]:
from keras.models import Sequential
from keras.layers import Dense
model = Sequential([
Dense(32, activation='relu', input_shape=(input_size,)),
Dense(64, activation='relu'),
Dense(128, activation='relu'),
Dense(num_classes, activation='softmax')
])
model.summary()
# In[42]:
utilities.print_weights_shape(model)
output_size * (input_size + 1) == number_parameters
# In[4]:
assert 32 * (input_size + 1) == 4816928
assert 64 * (32 + 1) == 2112
assert 128 * (64 + 1) == 8320
assert num_classes * (128 + 1) == 1290
# In[44]:
from keras.models import Sequential
from keras.layers import Conv2D
model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
Conv2D(64, (3, 3), activation='relu'),
Conv2D(128, (3, 3), activation='relu'),
Dense(num_classes, activation='softmax')
])
model.summary()
# In[45]:
utilities.print_weights_shape(model)
output_size * (input_size + 1) == number_parameters
i.e.,
output_channels * (input_channels * window_size + 1) == number_parameters
here window_size=3*3
# In[6]:
assert 32 * (3 * (3*3) + 1) == 896
assert 64 * (32 * (3*3) + 1) == 18496
assert 128 * (64 * (3*3) + 1) == 73856
assert num_classes * (128 + 1) == 1290
# In[46]:
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
# In[47]:
utilities.print_weights_shape(model)
# In[48]:
assert 32 * (3 * (3*3) + 1) == 896
assert 64 * (32 * (3*3) + 1) == 18496
assert 110 * 110 * 64 == 774400
assert 128 * (774400 + 1) == 99123328
assert num_classes * (128 + 1) == 1290
# In[ ]: