# # 8/19/2020
# A deep convolutional neural network is a network that has more than one layer. Each layer in a deep network receives its input from the preceding layer, with the very first layer receiving its input from the images used as training or test data.

# Here, you will create a network that has two convolutional layers.

from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten

model = Sequential()

# Add a convolutional layer (15 units)
model.add(Conv2D(15, kernel_size=2, activation='relu', 
                 input_shape=(img_rows, img_cols, 1)))
              
# Add another convolutional layer (5 units)
model.add(Conv2D(5, kernel_size=2, activation='relu'))

# Flatten and feed to output layer
model.add(Flatten())
model.add(Dense(3, activation='softmax'))
ade3082390d1cb7caaf7575d7944e5c25ea0096a
<mask token>

<mask token>
model.add(Conv2D(15, kernel_size=2, activation='relu', input_shape=(
    img_rows, img_cols, 1)))
model.add(Conv2D(5, kernel_size=2, activation='relu'))
model.add(Flatten())
model.add(Dense(3, activation='softmax'))

<mask token>
model = Sequential()
model.add(Conv2D(15, kernel_size=2, activation='relu', input_shape=(
    img_rows, img_cols, 1)))
model.add(Conv2D(5, kernel_size=2, activation='relu'))
model.add(Flatten())
model.add(Dense(3, activation='softmax'))

from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten
model = Sequential()
model.add(Conv2D(15, kernel_size=2, activation='relu', input_shape=(
    img_rows, img_cols, 1)))
model.add(Conv2D(5, kernel_size=2, activation='relu'))
model.add(Flatten())
model.add(Dense(3, activation='softmax'))

# # 8/19/2020
# A deep convolutional neural network is a network that has more than one layer. Each layer in a deep network receives its input from the preceding layer, with the very first layer receiving its input from the images used as training or test data.

# Here, you will create a network that has two convolutional layers.

from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten

model = Sequential()

# Add a convolutional layer (15 units)
model.add(Conv2D(15, kernel_size=2, activation='relu', 
                 input_shape=(img_rows, img_cols, 1)))
              
# Add another convolutional layer (5 units)
model.add(Conv2D(5, kernel_size=2, activation='relu'))

# Flatten and feed to output layer
model.add(Flatten())
model.add(Dense(3, activation='softmax'))