Input: [10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]
Output: import numpy as np
import tensorflow as tf
def create_model():
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation=’relu’, input_shape=(7, )),
tf.keras.layers.Dense(1)
])
model.compile(loss=’mean_squared_error’,
optimizer=’adam’,
metrics=[‘accuracy’])
return model
if __name__ == “__main__”:
model = create_model()
input_data = np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])
predictions = model.predict(input_data)
print(f”Predicted stock price for the next day is: {predictions[0][0]}”)
Prompt: Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Create a Python neural network model that takes in a given trajectory of stock prices and predicts the stock price for the next day.
### Input:
[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]
### Output:
import numpy as np
import tensorflow as tf
def create_model():
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation=’relu’, input_shape=(7, )),
tf.keras.layers.Dense(1)
])
model.compile(loss=’mean_squared_error’,
optimizer=’adam’,
metrics=[‘accuracy’])
return model
if __name__ == “__main__”:
model = create_model()
input_data = np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]])
predictions = model.predict(input_data)
print(f”Predicted stock price for the next day is: {predictions[0][0]}”)