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students.py
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import tensorflow as tf
from tensorflow.keras import layers
def student_555():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(2, (4,4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(4, (8,8), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_585():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(4, (4,4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(2, (8,8), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_393():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(4, (8,8), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(2, (4,4), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_525():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(4, (8,8), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(4, (4,4), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_789():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(4, (8,8), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(8, (4,4), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_1101():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(4, (4,4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(4, (8,8), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_282():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(1, (4,4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(4, (8,8), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_295():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(2, (4,4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(2, (8,8), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_159():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(1, (4,4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(2, (8,8), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_165():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(2, (4,4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(1, (8,8), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_141():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28, 28, 1)),
layers.MaxPooling2D(2),
layers.Conv2D(4, (4, 4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(4, (2, 2), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_233():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(2, (8,8), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(3, (4,4), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_425():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(2, (4,4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(3, (8,8), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_150():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(1, (4,4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(2, (8,8), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_216():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(1, (4,4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(3, (8,8), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_75():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28, 28, 1)),
layers.MaxPooling2D(2),
layers.Conv2D(2, (4, 4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(4, (2, 2), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28, 28, 1)),
layers.MaxPooling2D(2),
layers.Conv2D(2, (4, 4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(2, (2, 2), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student
def student_84():
student = tf.keras.Sequential([
layers.InputLayer(input_shape=(28,28,1)),
layers.Conv2D(1, (4,4), activation='relu'),
layers.MaxPooling2D(2),
layers.Conv2D(1, (8,8), activation='relu'),
layers.MaxPooling2D(4),
layers.Dense(1)
])
print(student.summary())
return student