![Please help fill in the missing code below in order for it run correctly. Areas that are
needed/missing are in highlighted in bold .
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
#1) Generate the synthetic data using the following Python code snippet.
# Generate synthetic data
N = 100
# Zeros form a Gaussian centered at (-1, -1)
x_zeros = np.random.multivariate_normal(mean=np.array((-1, -1)), cov=.1*np.eye(2),
size=(N//2,))
y_zeros = np.zeros((N//2,))
# Ones form a Gaussian centered at (1, 1)
x_ones = np.random.multivariate_normal(mean=np.array((1, 1)), cov=.1*np.eye(2),
size=(N//2,))
y_ones = np.ones((N//2,))
x_np = np.vstack([x_zeros, x_ones])
y_np = np.concatenate([y_zeros, y_ones])
# Plot x_zeros and x_ones on the same graph
plt.scatter(x_zeros[:,0], x_zeros[:,1], label='class 0')
plt.scatter(x_ones[:,0], x_ones[:,1], label='class 1')
plt.legend()
plt.show()
#3) Generate a TensorFlow graph.](https://image.slidesharecdn.com/pleasehelpfillinthemissingcodebelowinorderforitruncorrect-230220165100-e6b34386/85/Please-help-fill-in-the-missing-code-below-in-order-for-it-run-correct-docx-1-320.jpg)
![#4) Train the model, get the weights, and make predictions.
#5) Plot the predicted outputs on top of the data.
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
#1) Generate the synthetic data using the following Python code snippet.
# Generate synthetic data
N = 100
# Zeros form a Gaussian centered at (-1, -1)
x_zeros = np.random.multivariate_normal(mean=np.array((-1, -1)),
cov=.1*np.eye(2), size=(N//2,))
y_zeros = np.zeros((N//2,))
# Ones form a Gaussian centered at (1, 1)
x_ones = np.random.multivariate_normal(mean=np.array((1, 1)),
cov=.1*np.eye(2), size=(N//2,))
y_ones = np.ones((N//2,))
x_np = np.vstack([x_zeros, x_ones])
y_np = np.concatenate([y_zeros, y_ones])
# Plot x_zeros and x_ones on the same graph
plt.scatter(x_zeros[:,0], x_zeros[:,1], label='class 0')
plt.scatter(x_ones[:,0], x_ones[:,1], label='class 1')
plt.legend()](https://image.slidesharecdn.com/pleasehelpfillinthemissingcodebelowinorderforitruncorrect-230220165100-e6b34386/85/Please-help-fill-in-the-missing-code-below-in-order-for-it-run-correct-docx-3-320.jpg)
Please help fill in the missing code below in order for it run correct.docx
- 1. Please help fill in the missing code below in order for it run correctly. Areas that are needed/missing are in highlighted in bold . import numpy as np import tensorflow as tf import matplotlib.pyplot as plt #1) Generate the synthetic data using the following Python code snippet. # Generate synthetic data N = 100 # Zeros form a Gaussian centered at (-1, -1) x_zeros = np.random.multivariate_normal(mean=np.array((-1, -1)), cov=.1*np.eye(2), size=(N//2,)) y_zeros = np.zeros((N//2,)) # Ones form a Gaussian centered at (1, 1) x_ones = np.random.multivariate_normal(mean=np.array((1, 1)), cov=.1*np.eye(2), size=(N//2,)) y_ones = np.ones((N//2,)) x_np = np.vstack([x_zeros, x_ones]) y_np = np.concatenate([y_zeros, y_ones]) # Plot x_zeros and x_ones on the same graph plt.scatter(x_zeros[:,0], x_zeros[:,1], label='class 0') plt.scatter(x_ones[:,0], x_ones[:,1], label='class 1') plt.legend() plt.show() #3) Generate a TensorFlow graph.
- 2. with tf.name_scope("placeholders"): x = tf.constant(x_np, dtype=tf.float32) y = tf.constant(y_np, dtype=tf.float32) with tf.name_scope("weights"): W = tf.Variable(tf.random.normal((2, 1))) b = tf.Variable(tf.random.normal((1,))) with tf.name_scope("prediction"): y_logit = tf.squeeze(tf.matmul(x, W) + b) # the sigmoid gives the class probability of 1 y_one_prob = tf.sigmoid(y_logit) # Rounding P(y=1) will give the correct prediction. y_pred = tf.round(y_one_prob) with tf.name_scope("loss"): # Compute the cross-entropy term for each datapoint entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logit, labels=y) # Sum all contributions l = tf.reduce_sum(entropy) with tf.name_scope("optim"): train_op = tf.compat.v1.train.AdamOptimizer(.01).minimize(l) with tf.name_scope("summaries"): tf.compat.v1.summary.scalar("loss", l) merged = tf.compat.v1.summary.merge_all() train_writer = tf.compat.v1.summary.FileWriter('logistic-train', tf.compat.v1.get_default_graph())
- 3. #4) Train the model, get the weights, and make predictions. #5) Plot the predicted outputs on top of the data. import numpy as np import tensorflow as tf import matplotlib.pyplot as plt #1) Generate the synthetic data using the following Python code snippet. # Generate synthetic data N = 100 # Zeros form a Gaussian centered at (-1, -1) x_zeros = np.random.multivariate_normal(mean=np.array((-1, -1)), cov=.1*np.eye(2), size=(N//2,)) y_zeros = np.zeros((N//2,)) # Ones form a Gaussian centered at (1, 1) x_ones = np.random.multivariate_normal(mean=np.array((1, 1)), cov=.1*np.eye(2), size=(N//2,)) y_ones = np.ones((N//2,)) x_np = np.vstack([x_zeros, x_ones]) y_np = np.concatenate([y_zeros, y_ones]) # Plot x_zeros and x_ones on the same graph plt.scatter(x_zeros[:,0], x_zeros[:,1], label='class 0') plt.scatter(x_ones[:,0], x_ones[:,1], label='class 1') plt.legend()
- 4. plt.show() #3) Generate a TensorFlow graph. with tf.name_scope("placeholders"): x = tf.constant(x_np, dtype=tf.float32) y = tf.constant(y_np, dtype=tf.float32) with tf.name_scope("weights"): W = tf.Variable(tf.random.normal((2, 1))) b = tf.Variable(tf.random.normal((1,))) with tf.name_scope("prediction"): y_logit = tf.squeeze(tf.matmul(x, W) + b) # the sigmoid gives the class probability of 1 y_one_prob = tf.sigmoid(y_logit) # Rounding P(y=1) will give the correct prediction. y_pred = tf.round(y_one_prob) with tf.name_scope("loss"): # Compute the cross-entropy term for each datapoint entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logit, labels=y) # Sum all contributions l = tf.reduce_sum(entropy) with tf.name_scope("optim"): train_op = tf.compat.v1.train.AdamOptimizer(.01).minimize(l) with tf.name_scope("summaries"): tf.compat.v1.summary.scalar("loss", l)
- 5. merged = tf.compat.v1.summary.merge_all() train_writer = tf.compat.v1.summary.FileWriter('logistic-train', tf.compat.v1.get_default_graph()) #4) Train the model, get the weights, and make predictions. #5) Plot the predicted outputs on top of the data.