[FIXED] How to get the output of a keras model as numerical values, rather than a Tensor object?

Issue

This is probably quite a basic tensorflow/keras question but it is I can't seem to find it in the docs. I'm looking to retrieve the output of the hidden layer as numerical values for use in subsequent calculations. Here's the model

from io import StringIO
import pandas as pd
import numpy as np
import keras

data_str = """     
ti,z1,z2
0.0,1.000,0.000
0.1,0.606,0.373
0.2,0.368,0.564
0.3,0.223,0.647
0.4,0.135,0.669
0.5,0.082,0.656
0.6,0.050,0.624
0.7,0.030,0.583
0.8,0.018,0.539
0.9,0.011,0.494
1.0,0.007,0.451"""
data = pd.read_csv(StringIO(data_str), sep=',')

wd = r'/path/to/working/directory'
model_filename = os.path.join(wd, 'example1_with_keras.h5')

RUN = True
if RUN:
    model = keras.Sequential()
    model.add(keras.layers.Dense(3, activation='tanh', input_shape=(1, )))
    model.add(keras.layers.Dense(2, activation='tanh'))
    model.compile(optimizer='adam', loss='mean_squared_error')
    model.fit(data['ti'].values, data[['z1', 'z2']].values, epochs=30000)
    model.save(filepath=model_filename)
else:
    model = keras.models.load_model(model_filename)

outputs = model.layers[1].output
print(outputs)

This prints the following:

>>> Tensor("dense_2/Tanh:0", shape=(?, 2), dtype=float32)

How can I get the output as a np.array rather than a Tensor object?

Solution

Using model.layer[1].output does not produce an output, it simply returns the tensor definition of the output. In order to actually produce an output, you need to run your data through the model and specify model.layer[1].output as the output.

You can do this by using tf.keras.backend.function (documentation), which will return Numpy arrays. A similar question to yours can be found here.

The following should work for your example if you only want the output from model.layers[1].output and if you convert your data to a Numpy array for input:

from keras import backend as K
outputs = [model.layers[1].output]
functor = K.function([model.input, K.learning_phase()], outputs)
layer_outs = functor([data, 1.])

Answered By - Luke DeLuccia