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- # SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 120.
- fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))
- fc1_b = tf.Variable(tf.zeros(120))
- fc1 = tf.matmul(fc0, fc1_W) + fc1_b
- # SOLUTION: Activation.
- fc1 = tf.nn.relu(fc1)
- fc1 = tf.nn.dropout(fc1, keep_prob)
- # SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84.
- fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))
- fc2_b = tf.Variable(tf.zeros(84))
- fc2 = tf.matmul(fc1, fc2_W) + fc2_b
- # SOLUTION: Activation.
- fc2 = tf.nn.relu(fc2)
- fc2 = tf.nn.dropout(fc2, keep_prob)
- # SOLUTION: Layer 5: Fully Connected. Input = 84. Output = 10.
- fc3_W = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))
- fc3_b = tf.Variable(tf.zeros(43))
- logits = tf.matmul(fc2, fc3_W) + fc3_b
- return logits
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