deep learning tensors and layers and encoders.txt

first_layers = model.layers[0]

print(first_layer.input)
print(first_layer.ouput)
print(first_layer.weights)


tensors are 2 dimensions

T2=[[1,2,3],
[4,5,6],
[7,8,9]]

A three dimension tensors is an array of matrices.


import keras.backend as K

inp= model.layers[0].input
out= model.layers[0].output

inp_to_out = K.function([inp],[out])

print(inp_to_out([X_train]))

autoencoders are models have the same inputs as outputs
1) dimensionality reduction
a. smaller dimensional space representation of our inputs
2) De-noising data
a. if trained with clean data, irrelevant noise will be filtered out during reconstruction.
3. Anomaly detection:
a. a poor reconstruction will result when the model is fed with unseen inputs


autoencoder= Sequential()

autoencoder.add(Dense(4, input_shape(100,), activation='relu'))

autoencoder.add(Dense(100,activation='sigmoid')

autoencoder.compile(optimizer='adam', loss='binary_crossentropy')


encoder=Sequential()
encoder.add(autoencoder.layers[0])

encoder.predict(X_test)


>>>>
# Import keras backend
import keras.backend as K

# Input tensor from the 1st layer of the model
inp =  model.layers[0].input

# Output tensor from the 1st layer of the model
out = model.layers[0].output

# Define a function from inputs to outputs
inp_to_out = K.function([inp], [out])

# Print the results of passing X_test through the 1st layer
print(inp_to_out([X_test]))

for i in range(0, 21):
  	# Train model for 1 epoch
    h = model.fit(X_train, y_train, batch_size=16, epochs=1, verbose=0)
    if i%4==0: 
      # Get the output of the first layer
      layer_output = inp_to_out([X_test])[0]
      
      # Evaluate model accuracy for this epoch
      test_accuracy = model.evaluate(X_test, y_test)[1] 
      
      # Plot 1st vs 2nd neuron output
      plot()

# Start with a sequential model
autoencoder = Sequential()

# Add a dense layer with the original image as input
autoencoder.add(Dense(32, input_shape=(784, ), activation="relu"))

# Add an output layer with as many nodes as the image
autoencoder.add(Dense(784, activation="sigmoid"))

# Compile your model
autoencoder.compile(optimizer="adadelta", loss="binary_crossentropy")

# Take a look at your model structure
autoencoder.summary()


# Build your encoder
encoder = Sequential()
encoder.add(autoencoder.layers[0])

# Encode the images and show the encodings
preds = encoder.predict(X_test_noise)
show_encodings(preds)

# Build your encoder
encoder = Sequential()
encoder.add(autoencoder.layers[0])

# Encode the images and show the encodings
preds = encoder.predict(X_test_noise)
show_encodings(preds)

# Predict on the noisy images with your autoencoder
decoded_imgs = autoencoder.predict(X_test_noise)

# Plot noisy vs decoded images
compare_plot(X_test_noise, decoded_imgs)