CODEWITHSUNDEEP
pythontensorflowmachine-learningkerasneural-network
James
James

Reputation: 23

Simple single layer neural network

I'm writing a very simple network:

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

training_data = np.array([[1, 1, 1], [2, 3, 1], [0, -1, 4], [0, 3, 0], [10, -6, 8], [-3, -12, 4]])
testing_data = np.array([6, 11, 1, 9, 10, -38])

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(units = 1, activation = tf.keras.activations.relu, input_shape = (3, )))
model.compile(optimizer = tf.keras.optimizers.RMSprop(0.001), loss = tf.keras.losses.mean_squared_error, metrics = tf.keras.metrics.mean_squared_error)
model.summary()
model.fit(training_data, testing_data, epochs = 1, verbose = 'False')
print("Traning completed.")
model.predict(np.array([1, 1, 1]))

The goal is to train the weights like : aX + bY + cZ = (output)
But I get the error

ValueError: Input 0 of layer sequential_54 is incompatible with the layer: expected axis -1 of input shape to have value 3 but received input with shape [None, 1]


I can't make scene of the dimensions, there is something I'm doing wrong! Any help?

Upvotes: 2

Views: 419

Answers (1)

Parthasarathy Subburaj
Parthasarathy Subburaj

Reputation: 4264

In Keras when you specify the input shape batch size is ignored, please refer here for more details. Your declaration of input_shape = (3, ) is correct, but when you do inference you need to account for the batch size as well by adding an extra dimension for the same so instead of np.array([1, 1, 1]) you need to have np.array([[1, 1, 1]]).

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

training_data = np.array([[1, 1, 1], [2, 3, 1], [0, -1, 4], [0, 3, 0], [10, -6, 8], [-3, -12, 4]])
testing_data = np.array([6, 11, 1, 9, 10, -38])

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(units = 1, activation = tf.keras.activations.relu, input_shape = (3,)))
model.compile(optimizer = tf.keras.optimizers.RMSprop(0.001), loss = tf.keras.losses.mean_squared_error, metrics = [tf.keras.metrics.mean_squared_error])
model.summary()

model.fit(training_data, testing_data, epochs = 1, verbose = 'False')
print("Traning completed.")
model.predict(np.array([[1, 2, 1]]))

array([[0.08026636]], dtype=float32)

Upvotes: 2

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