Das deutsche Python-Forum

Code: Alles auswählen

import tensorflow as tf
import tensorflow.keras
import numpy as np

X_train = (np.random.sample((1,4, 3, 5, 3)))
y_train =  (np.random.sample((106 ,3)))
print(X_train.shape)
print(y_train)
 

Code: Alles auswählen

mport tensorflow as tf
import keras
from keras import layers
import tensorflow as tf
import keras
from keras import layers
 

# Define Sequential model with 3 layers
model = keras.Sequential(
    [
        layers.Dense(2, activation="relu", name="layer1"),
        layers.Dense(3, activation="relu", name="layer2"),
        layers.Dense(4, name="layer3"),
    ]
)
# Call model on a test input
x = tf.ones((3, 3))
y = model(x)
 


# Create 3 layers
layer1 = layers.Dense(2, activation="relu", name="layer1")
layer2 = layers.Dense(3, activation="relu", name="layer2")
layer3 = layers.Dense(4, name="layer3")

# Call layers on a test input
x = tf.ones((3, 3))
y = layer3(layer2(layer1(x)))
 
 
 
 
 
 


model = keras.Sequential(
    [
        layers.Dense(2, activation="relu"),
        layers.Dense(3, activation="relu"),
        layers.Dense(4),
    ]
)
 

model.layers

 

model = keras.Sequential()
model.add(layers.Dense(2, activation="relu"))
model.add(layers.Dense(3, activation="relu"))
model.add(layers.Dense(4))
 

model.pop()
print(len(model.layers))  # 2

 
model = keras.Sequential(name="my_sequential")
model.add(layers.Dense(2, activation="relu", name="layer1"))
model.add(layers.Dense(3, activation="relu", name="layer2"))
model.add(layers.Dense(4, name="layer3"))
 
  


layer = layers.Dense(3)
layer.weights  # Empty

[]
 

# Call layer on a test input
x = tf.ones((1, 4))
y = layer(x)
layer.weights  # Now it has weights, of shape (4, 3) and (3,)

#[<tf.Variable 'dense_6/kernel:0' shape=(4, 3) dtype=float32, numpy=
# array([[ 0.1752373 ,  0.47623062,  0.24374962],
#        [-0.0298934 ,  0.50255656,  0.78478384],
#        [-0.58323103, -0.56861055, -0.7190975 ],
#        [-0.3191281 , -0.23635858, -0.8841506 ]], dtype=float32)>,
# <tf.Variable 'dense_6/bias:0' shape=(3,) dtype=float32, numpy=array([0., 0., 0.], dtype=float32)>]
 

model = keras.Sequential(
    [
        layers.Dense(2, activation="relu"),
        layers.Dense(3, activation="relu"),
        layers.Dense(4),
    ]
)  # No weights at this stage!

# At this point, you can't do this:
# model.weights

# You also can't do this:
# model.summary()

# Call the model on a test input
x = tf.ones((1, 4))
y = model(x)
print("Number of weights after calling the model:", len(model.weights))  # 6

 
 


model.summary()

Model: "sequential_3"
#_________________________________________________________________
# Layer (type)                Output Shape              Param #   
#=================================================================
# dense_7 (Dense)             (1, 2)                    10        
                                                                 
# dense_8 (Dense)             (1, 3)                    9         
                                                                 
# dense_9 (Dense)             (1, 4)                    16        
                                                                 
 
#Total params: 35 (140.00 Byte)
#Trainable params: 35 (140.00 Byte)
#Non-trainable params: 0 (0.00 Byte)
 

model = keras.Sequential()
model.add(keras.Input(shape=(4,)))
model.add(layers.Dense(2, activation="relu"))

model.summary()

Model: "sequential_4"
 
# Layer (type)                Output Shape              Param #   
#=================================================================
# dense_10 (Dense)            (None, 2)                 10        
                                                            
 
#Total params: 10 (40.00 Byte)
#Trainable params: 10 (40.00 Byte)
#Non-trainable params: 0 (0.00 Byte)
# 

#model.layers

 
#A simple alternative is to just pass an input_shape argument to your first layer:


model = keras.Sequential()
model.add(layers.Dense(2, activation="relu", input_shape=(4,)))

model.summary()

Model: "sequential_5"
 
                                                                 
 
#Total params: 10 (40.00 Byte)
#Trainable params: 10 (40.00 Byte)
#Non-trainable params: 0 (0.00 Byte)
#_________________________________________________________________
 
 

 

model = keras.Sequential()
model.add(keras.Input(shape=(250, 250, 3)))  # 250x250 RGB images
model.add(layers.Conv2D(32, 5, strides=2, activation="relu"))
model.add(layers.Conv2D(32, 3, activation="relu"))
model.add(layers.MaxPooling2D(3))

# Can you guess what the current output shape is at this point? Probably not.
# Let's just print it:
model.summary()

# The answer was: (40, 40, 32), so we can keep downsampling...

model.add(layers.Conv2D(32, 3, activation="relu"))
model.add(layers.Conv2D(32, 3, activation="relu"))
model.add(layers.MaxPooling2D(3))
model.add(layers.Conv2D(32, 3, activation="relu"))
model.add(layers.Conv2D(32, 3, activation="relu"))
model.add(layers.MaxPooling2D(2))

# And now?
model.summary()

# Now that we have 4x4 feature maps, time to apply global max pooling.
model.add(layers.GlobalMaxPooling2D())

# Finally, we add a classification layer.
model.add(layers.Dense(10))

Model: "sequential_6"
# Layer (type)                Output Shape              Param #   
#=================================================================
# conv2d (Conv2D)             (None, 123, 123, 32)      2432      
                                                                 
# conv2d_1 (Conv2D)           (None, 121, 121, 32)      9248      
                                                                 
# max_pooling2d (MaxPooling2  (None, 40, 40, 32)        0         
# D)                                                              
                                                                 
 
#Total params: 11680 (45.62 KB)
#Trainable params: 11680 (45.62 KB)
#Non-trainable params: 0 (0.00 Byte)
#_________________________________________________________________
#Model: "sequential_6"
 
#=================================================================
#conv2d (Conv2D)             (None, 123, 123, 32)      2432      
                                                                 
# conv2d_1 (Conv2D)           (None, 121, 121, 32)      9248      
                                                                 
# max_pooling2d (MaxPooling2  (None, 40, 40, 32)        0         
# D)                                                              
                                                                 
# conv2d_2 (Conv2D)           (None, 38, 38, 32)        9248      
                                                                 
# conv2d_3 (Conv2D)           (None, 36, 36, 32)        9248      
                                                                 
# max_pooling2d_1 (MaxPoolin  (None, 12, 12, 32)        0         
# g2D)                                                            
                                                                 
# conv2d_4 (Conv2D)           (None, 10, 10, 32)        9248      
                                                                 
# conv2d_5 (Conv2D)           (None, 8, 8, 32)          9248      
                                                                 
 #max_pooling2d_2 (MaxPoolin  (None, 4, 4, 32)          0         
# g2D)                                                            
                                                                 
#================================================================
#Total params: 48672 (190.12 KB)
#Trainable params: 48672 (190.12 KB)
#Non-trainable params: 0 (0.00 Byte)
 

 
initial_model = keras.Sequential(
    [
        keras.Input(shape=(250, 250, 3)),
        layers.Conv2D(32, 5, strides=2, activation="relu"),
        layers.Conv2D(32, 3, activation="relu"),
        layers.Conv2D(32, 3, activation="relu"),
    ]
)
feature_extractor = keras.Model(
    inputs=initial_model.inputs,
    outputs=[layer.output for layer in initial_model.layers],
)

# Call feature extractor on test input.
x = tf.ones((1, 250, 250, 3))
features = feature_extractor(x)
 


initial_model = keras.Sequential(
    [
        keras.Input(shape=(250, 250, 3)),
        layers.Conv2D(32, 5, strides=2, activation="relu"),
        layers.Conv2D(32, 3, activation="relu", name="my_intermediate_layer"),
        layers.Conv2D(32, 3, activation="relu"),
    ]
)
feature_extractor = keras.Model(
    inputs=initial_model.inputs,
    outputs=initial_model.get_layer(name="my_intermediate_layer").output,
)
# Call feature extractor on test input.
x = tf.ones((1, 250, 250, 3))
features = feature_extractor(x)
 
#model = keras.Sequential([ keras.Input(shape=(784)),
#    layers.Dense(32, activation='relu'),
#    layers.Dense(32, activation='relu'),
#    layers.Dense(10)])

# Presumably you would want to first load pre-trained weights.
#
# Freeze all layers except the last one.
for layer in model.layers[:-1]:
  layer.trainable = False

# Recompile and train (this will only update the weights of the last layer).
  

# Load a convolutional base with pre-trained weights
base_model = keras.applications.Xception(
    weights='imagenet',
    include_top=False,
    pooling='avg')

# Freeze the base model
base_model.trainable = False

# Use a Sequential model to add a trainable classifier on top
model = keras.Sequential([
    base_model,
    layers.Dense(1000),

Code: Alles auswählen

mport pandas as pd
import tensorflow as tf
print("TensorFlow version:", tf.__version__)

import tensorflow as tf
import pandas as pd
import matplotlib
from matplotlib import pyplot as plt
print("TensorFlow version:", tf.__version__)
# Set a random seed for reproducible results 
tf.random.set_seed(45)
url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'
column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',
                'Acceleration', 'Model Year', 'Origin']

dataset = pd.read_csv(url, names=column_names, na_values='?', comment='\t',
                          sep=' ', skipinitialspace=True)

dataset = dataset.dropna()
dataset_tf = tf.convert_to_tensor(dataset, dtype=tf.float32)
dataset.tail()
dataset_shuffled = tf.random.shuffle(dataset_tf, seed=22)
train_data, test_data = dataset_shuffled[100:], dataset_shuffled[:100]
x_train, y_train = train_data[:, 1:], train_data[:, 0]
x_test, y_test = test_data[:, 1:], test_data[:, 0]

def onehot_origin(x):
  origin = tf.cast(x[:, -1], tf.int32)
  # Use `origin - 1` to account for 1-indexed feature
  origin_oh = tf.one_hot(origin - 1, 3)
  x_ohe = tf.concat([x[:, :-1], origin_oh], axis = 1)
  return x_ohe

x_train_ohe, x_test_ohe = onehot_origin(x_train), onehot_origin(x_test)
x_train_ohe.numpy()

class Normalize(tf.Module):
  def __init__(self, x):
    # Initialize the mean and standard deviation for normalization
    self.mean = tf.math.reduce_mean(x, axis=0)
    self.std = tf.math.reduce_std(x, axis=0)

  def norm(self, x):
    # Normalize the input
    return (x - self.mean)/self.std

  def unnorm(self, x):
    # Unnormalize the input
    return (x * self.std) + self.mean


norm_x = Normalize(x_train_ohe)
norm_y = Normalize(y_train)
x_train_norm, y_train_norm = norm_x.norm(x_train_ohe), norm_y.norm(y_train)
x_test_norm, y_test_norm = norm_x.norm(x_test_ohe), norm_y.norm(y_test)

class LinearRegression(tf.Module):

  def __init__(self):
    self.built = False

  @tf.function
  def __call__(self, x):
    # Initialize the model parameters on the first call
    if not self.built:
      # Randomly generate the weight vector and bias term
      rand_w = tf.random.uniform(shape=[x.shape[-1], 1])
      rand_b = tf.random.uniform(shape=[])
      self.w = tf.Variable(rand_w)
      self.b = tf.Variable(rand_b)
      self.built = True
    y = tf.add(tf.matmul(x, self.w), self.b)
    return tf.squeeze(y, axis=1)

lin_reg = LinearRegression()
prediction = lin_reg(x_train_norm[:1])
prediction_unnorm = norm_y.unnorm(prediction)
prediction_unnorm.numpy()

def mse_loss(y_pred, y):
  return tf.reduce_mean(tf.square(y_pred - y))

batch_size = 64
train_dataset = tf.data.Dataset.from_tensor_slices((x_train_norm, y_train_norm))
train_dataset = train_dataset.shuffle(buffer_size=x_train.shape[0]).batch(batch_size)
test_dataset = tf.data.Dataset.from_tensor_slices((x_test_norm, y_test_norm))
test_dataset = test_dataset.shuffle(buffer_size=x_test.shape[0]).batch(batch_size)

# Set training parameters
epochs = 100
learning_rate = 0.01
train_losses, test_losses = [], []
 
# Format training loop
for epoch in range(epochs):
  batch_losses_train, batch_losses_test = [], []

  # Iterate through the training data
  for x_batch, y_batch in train_dataset:
    with tf.GradientTape() as tape:
      y_pred_batch = lin_reg(x_batch)
      batch_loss = mse_loss(y_pred_batch, y_batch)
    # Update parameters with respect to the gradient calculations
    grads = tape.gradient(batch_loss, lin_reg.variables)
    for g,v in zip(grads, lin_reg.variables):
      v.assign_sub(learning_rate * g)


    batch_losses_train.append(batch_loss)

  # Iterate through the testing data
  for x_batch, y_batch in test_dataset:
    y_pred_batch = lin_reg(x_batch)
    batch_loss = mse_loss(y_pred_batch, y_batch)
    # Keep track of batch-level testing performance 
    batch_losses_test.append(batch_loss)

  # Keep track of epoch-level model performance
  train_loss = tf.reduce_mean(batch_losses_train)
  test_loss = tf.reduce_mean(batch_losses_test)
  train_losses.append(train_loss)
  test_losses.append(test_loss)

  if epoch % 10 == 0:
    print(f'Mean squared error for step {epoch}: {train_loss.numpy():0.3f}')

# Output final losses
print(f"\nFinal train loss: {train_loss:0.3f}")
print(f"Final test loss: {test_loss:0.3f}")

matplotlib.rcParams['figure.figsize'] = [9, 6]

plt.plot(range(epochs), train_losses, label = "Training loss")
plt.plot(range(epochs), test_losses, label = "Testing loss")
plt.xlabel("Epoch")
plt.ylabel("Mean squared error loss")
plt.legend()
plt.title("MSE loss vs training iterations");
plt.show()

Code: Alles auswählen

print("Neueste Version  :",  tf.version.VERSION)  
a=  tf.compat.v1.placeholder  
# creating a tensorflow graph 
graph = tf.Graph() 
with graph.as_default(): 
    
    # creating a placeholder 
    a=  tf.compat.v1.placeholder(tf.float64, shape=(3, 3), name='tensor1') 
      
    # creating an operation 
    b = a *3 
  
# array1 will be fed into 'a' 
array1 =      [[1, 2, 3], [4, 5, 6], [7, 8, 9]] 
  
# Creating a session, and running the graph 
with tf.compat.v1.Session(graph=graph) as session: 
      
    # run the session until it reaches node b, 
    # then input an array of values into a 
    operation_res = session.run(b, feed_dict={a: array1}) 
    print("after executing the operation: ") 
    print(operation_res) 

Code: Alles auswählen

import tensorflow as tf
x = tf.constant([[1., 2., 3.],
                 [4., 5., 6.]])

print(x)
print(x.shape)
print(x.dtype)
 


x + x

5 * x

x @ tf.transpose(x)

tf.concat([x, x, x], axis=0)

tf.nn.softmax(x, axis=-1)

tf.reduce_sum(x)
tf.convert_to_tensor([1,2,3])

tf.reduce_sum([1,2,3])
 


if tf.config.list_physical_devices('GPU'):
  print("TensorFlow **IS** using the GPU")
else:
  print("TensorFlow **IS NOT** using the GPU")
 

 

var = tf.Variable([0.0, 0.0, 0.0])

var.assign([1, 2, 3])

var.assign_add([1, 1, 1])
 

 
 

 
x = tf.Variable(1.0)

def f(x):
  y = x**2 + 2*x - 5
  return y

print(f(x))
#At x = 1.0, y = f(x) = (1**2 + 2*1 - 5) = -2.

 
with tf.GradientTape() as tape:
  y = f(x)

g_x = tape.gradient(y, x)  # g(x) = dy/dx

g_x
  
 

@tf.function
def my_func(x):
  print('Tracing.\n')
  return tf.reduce_sum(x)
 

x = tf.constant([1, 2, 3])
my_func(x)
 
x = tf.constant([10, 9, 8])
my_func(x)
 

x = tf.constant([10.0, 9.1, 8.2], dtype=tf.float32)
my_func(x)
 
  

 
 
 

class MyModule(tf.Module):
  def __init__(self, value):
    self.weight = tf.Variable(value)

  @tf.function
  def multiply(self, x):
    return x * self.weight

mod = MyModule(3)
mod.multiply(tf.constant([1, 2, 3]))
 


save_path = './saved'
 

  

 

import matplotlib
from matplotlib import pyplot as plt

matplotlib.rcParams['figure.figsize'] = [9, 6]

x = tf.linspace(-2, 2, 201)
x = tf.cast(x, tf.float32)

def f(x):
  y = x**2 + 2*x - 5
  return y

y = f(x) + tf.random.normal(shape=[201])

plt.plot(x.numpy(), y.numpy(), '.', label='Data')
plt.plot(x, f(x), label='Ground truth')
plt.legend();
 ###################################################
class Model(tf.Module):

  def __init__(self):
    # Randomly generate weight and bias terms
    rand_init = tf.random.uniform(shape=[3], minval=0., maxval=5., seed=22)
    # Initialize model parameters
    self.w_q = tf.Variable(rand_init[0])
    self.w_l = tf.Variable(rand_init[1])
    self.b = tf.Variable(rand_init[2])

  @tf.function
  def __call__(self, x):
    # Quadratic Model : quadratic_weight * x^2 + linear_weight * x + bias
    return self.w_q * (x**2) + self.w_l * x + self.b
 

quad_model = Model()

def plot_preds(x, y, f, model, title):
  plt.figure()
  plt.plot(x, y, '.', label='Data')
  plt.plot(x, f(x), label='Ground truth')
  plt.plot(x, model(x), label='Predictions')
  plt.title(title)
  plt.legend()

plot_preds(x, y, f, quad_model, 'Before training')
 
  

def mse_loss(y_pred, y):
  return tf.reduce_mean(tf.square(y_pred - y))
  

batch_size = 32
dataset = tf.data.Dataset.from_tensor_slices((x, y))
dataset = dataset.shuffle(buffer_size=x.shape[0]).batch(batch_size)

# Set training parameters
epochs = 100
learning_rate = 0.01
losses = []

# Format training loop
for epoch in range(epochs):
  for x_batch, y_batch in dataset:
    with tf.GradientTape() as tape:
      batch_loss = mse_loss(quad_model(x_batch), y_batch)
    # Update parameters with respect to the gradient calculations
    grads = tape.gradient(batch_loss, quad_model.variables)
    for g,v in zip(grads, quad_model.variables):
        v.assign_sub(learning_rate*g)
  # Keep track of model loss per epoch
  loss = mse_loss(quad_model(x), y)
  losses.append(loss)
  if epoch % 10 == 0:
    print(f'Mean squared error for step {epoch}: {loss.numpy():0.3f}')

# Plot model results
print("\n")
plt.plot(range(epochs), losses)
plt.xlabel("Epoch")
plt.ylabel("Mean Squared Error (MSE)")
plt.title('MSE loss vs training iterations');
 


plot_preds(x, y, f, quad_model, 'After training')
  
 

new_model = tf.keras.Sequential([
    tf.keras.layers.Lambda(lambda x: tf.stack([x, x**2], axis=1)),
    tf.keras.layers.Dense(units=1, kernel_initializer=tf.random.normal)])

new_model.compile(
    loss=tf.keras.losses.MSE,
    optimizer=tf.keras.optimizers.SGD(learning_rate=0.01))

history = new_model.fit(x, y,
                        epochs=100,
                        batch_size=32,
                        verbose=0)

  
plt.plot(history.history['loss'])
plt.xlabel('Epoch')
plt.ylim([0, max(plt.ylim())])
plt.ylabel('Loss [Mean Squared Error]')
plt.title('Keras training progress');

plot_preds(x, y, f, new_model, 'After Training: Keras')
plt.show()

Code: Alles auswählen

import matplotlib.pyplot as plt
import networkx as nx
from networkx import Graph


class PrintGraph(Graph):
    """
    Example subclass of the Graph class.

    Prints activity log to file or standard output.
    """

    def __init__(self, data=None, name="", file=None, **attr):
        super().__init__(data=data, name=name, **attr)
        if file is None:
            import sys

            self.fh = sys.stdout
        else:
            self.fh = open(file, "w")

    def add_node(self, n, attr_dict=None, **attr):
        super().add_node(n, attr_dict=attr_dict, **attr)
        self.fh.write(f"Add node: {n}\n")

    def add_nodes_from(self, nodes, **attr):
        for n in nodes:
            self.add_node(n, **attr)

    def remove_node(self, n):
        super().remove_node(n)
        self.fh.write(f"Remove node: {n}\n")

    def remove_nodes_from(self, nodes):
        for n in nodes:
            self.remove_node(n)

    def add_edge(self, u, v, attr_dict=None, **attr):
        super().add_edge(u, v, attr_dict=attr_dict, **attr)
        self.fh.write(f"Add edge: {u}-{v}\n")

    def add_edges_from(self, ebunch, attr_dict=None, **attr):
        for e in ebunch:
            u, v = e[0:2]
            self.add_edge(u, v, attr_dict=attr_dict, **attr)

    def remove_edge(self, u, v):
        super().remove_edge(u, v)
        self.fh.write(f"Remove edge: {u}-{v}\n")

    def remove_edges_from(self, ebunch):
        for e in ebunch:
            u, v = e[0:2]
            self.remove_edge(u, v)

    def clear(self):
        super().clear()
        self.fh.write("Clear graph\n")


G = PrintGraph()
G.add_node("foo")
G.add_nodes_from("bar", weight=18)
G.remove_node("b")
G.remove_nodes_from("ar")
print("Nodes in G: ", G.nodes(data=True))
G.add_edge(0, 1, weight=10)
print("Edges in G: ", G.edges(data=True))
G.remove_edge(0, 1)
G.add_edges_from(zip(range(3), range(1, 4)), weight=10)
print("Edges in G: ", G.edges(data=True))
G.remove_edges_from(zip(range(3), range(1, 4)))
print("Edges in G: ", G.edges(data=True))

G = PrintGraph()
nx.add_path(G, range(10))
nx.add_star(G, range(0,20))      #1  Edges,  2 Knoten)
pos = nx.spring_layout(G, seed= 225)  # Seed for reproducible layout
nx.draw(G, pos)
plt.show()

Code: Alles auswählen

#  neuronales netzwerk  einfachste Struktur

import numpy as np

# Eingabedaten
X = np.array([[0,1, 1],
              [0, 1, 1],
              [1, 0, 1],
              [1, 1, 1]])

# Erwartete Ausgabedaten
y = np.array([[0],
              [1],
              [1],
              [0]])
# Anzahl der Eingabe- und Ausgabeneuronen
input_neurons = X.shape[1]
output_neurons = y.shape[1]

# Anzahl der Neuronen in der versteckten Schicht
hidden_neurons = 10

# Definition der Aktivierungsfunktion (Sigmoid)
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# Initialisierung der Gewichtungsmatrizen
# Gewichte zwischen Eingabe- und versteckter Schicht
weights_input_hidden = np.random.rand(input_neurons, hidden_neurons)
# Gewichte zwischen versteckter Schicht und Ausgabe
weights_hidden_output = np.random.rand(hidden_neurons, output_neurons)

# Anzahl der Epochen für das Training
epochs = 5                            # epoch  5-5000


# Trainingschleife
for epoch in range(epochs):
    # Vorwärtspropagation (Forward Propagation)
    hidden_layer_input = np.dot(X, weights_input_hidden)
    hidden_layer_output = sigmoid(hidden_layer_input)
    print("HIDDENLAYER", hidden_layer_output)
    output = sigmoid(np.dot(hidden_layer_output, weights_hidden_output))
    
    # Berechnung des Fehlers (Error)
    error = y - output
  
# Definition der Ableitung der Aktivierungsfunktion (Sigmoid)
def sigmoid_derivative(x):
    return x * (1 - x)

# Initialisierung der Gewichtungsmatrizen
# Gewichte zwischen Eingabe- und versteckter Schicht
weights_input_hidden = np.random.rand(input_neurons, hidden_neurons)
# Gewichte zwischen versteckter Schicht und Ausgabe
weights_hidden_output = np.random.rand(hidden_neurons, output_neurons)

# Anzahl der Epochen für das Training
epochs = 5                  #5 -5000          

# Trainingschleife
for epoch in range(epochs):
    # Vorwärtspropagation (Forward Propagation)
    hidden_layer_input = np.dot(X, weights_input_hidden)
    hidden_layer_output = sigmoid(hidden_layer_input)
    output = sigmoid(np.dot(hidden_layer_output, weights_hidden_output))
    
    # Berechnung des Fehlers (Error)
    error = y - output
   # print("Error        ",error  ) 
    # Rückwärtspropagation (Backpropagation) und Gewichtsanpassung
    d_weights_hidden_output = np.dot(hidden_layer_output.T, error * sigmoid_derivative(output))
    d_weights_input_hidden = np.dot(X.T, np.dot(error * sigmoid_derivative(output), weights_hidden_output.T) * sigmoid_derivative(hidden_layer_output))
    
    weights_hidden_output += d_weights_hidden_output
    weights_input_hidden += d_weights_input_hidden
    print(weights_input_hidden)
# Ausgabe der finalen Vorhersage
print(output)

Code: Alles auswählen

mport os
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
#Prepare the Dataset
#The Cora dataset consists of 2,708 scientific papers classified into one of seven classes.
#The citation network consists of 5,429 links.
#Each paper has a binary word vector of size 1,433, indicating the presence of a corresponding word.
#Download the dataset
#The dataset has two tap-separated files: cora.cites and cora.content.
#The cora.cites includes the citation records with two columns: cited_paper_id (target) and citing_paper_id (source).
#The cora.content includes the paper content records with 1,435 columns: paper_id, subject, and 1,433 binary features.
#Let's download the dataset.

zip_file = keras.utils.get_file(
    fname="cora.tgz",
    origin="https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz",
    extract=True,
)
data_dir = os.path.join(os.path.dirname(zip_file), "cora")
#Process and visualize the dataset
#Then we load the citations data into a Pandas DataFrame.

citations = pd.read_csv(
    os.path.join(data_dir, "cora.cites"),
    sep="\t",
    header=None,
    names=["target", "source"],
)
print("Citations shape:", citations.shape)
#Citations shape: (5429, 2)
#Now we display a sample of the citations DataFrame. The target column includes the paper ids cited by the paper ids in the source column.

citations.sample(frac=1).head()
#pers data into a Pandas DataFrame.

column_names = ["paper_id"] + [f"term_{idx}" for idx in range(1433)] + ["subject"]
papers = pd.read_csv(
    os.path.join(data_dir, "cora.content"), sep="\t", header=None, names=column_names,
)
print("Papers shape:", papers.shape)
print(papers.sample(5).T)
print(papers.subject.value_counts())
 
class_values = sorted(papers["subject"].unique())
class_idx = {name: id for id, name in enumerate(class_values)}
paper_idx = {name: idx for idx, name in enumerate(sorted(papers["paper_id"].unique()))}

papers["paper_id"] = papers["paper_id"].apply(lambda name: paper_idx[name])
citations["source"] = citations["source"].apply(lambda name: paper_idx[name])
citations["target"] = citations["target"].apply(lambda name: paper_idx[name])
papers["subject"] = papers["subject"].apply(lambda value: class_idx[value])
#Now let's visualize the citation graph. Each node in the graph represents a paper, and the color of the node corresponds to its subject. Note that we only show a sample of the papers in the dataset.
plt.figure(figsize=(10, 10))
colors = papers["subject"].tolist()
cora_graph = nx.from_pandas_edgelist(citations.sample(n=1500))
subjects = list(papers[papers["paper_id"].isin(list(cora_graph.nodes))]["subject"])
nx.draw_spring(cora_graph, node_size=15, node_color=subjects)
plt.show()

Code: Alles auswählen

#                   Die   implizite  pyplot-Schnittstelle
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4], [0, 0.5, 1, 0.2])
plt.show()

Code: Alles auswählen

# GPA anwenden    

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-2 * np.pi, 2 * np.pi, 70, endpoint=True)

F1 = X * np.sin(X) + np.cos(X)

ax = plt.gca()                              # CGA
ax.spines['top'].set_color('red')
ax.spines['right'].set_color('red')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))

# Labelung der X-Achse:
plt.xticks( [-6.28, -3.14, 3.14, 6.28],
        [r'$-2\pi$', r'$-\pi$', r'$+\pi$', r'$+2\pi$'])
plt.yticks([-3, -1, 0, +1, 3])
plt.plot(X, F1)
plt.show()

Code: Alles auswählen

mport matplotlib.pyplot as plt
import numpy as np

import matplotlib
import matplotlib as mpl


vegetables = ["cucumber", "tomato", "lettuce", "asparagus",
              "potato", "wheat", "barley"]
farmers = ["Farmer Joe", "Upland Bros.", "Smith Gardening",
           "Agrifun", "Organiculture", "BioGoods Ltd.", "Cornylee Corp."]

harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],
                    [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],
                    [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],
                    [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],
                    [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],
                    [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],
                    [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])


fig, ax = plt.subplots()
im = ax.imshow(harvest)

# Show all ticks and label them with the respective list entries
ax.set_xticks(np.arange(len(farmers)), labels=farmers)
ax.set_yticks(np.arange(len(vegetables)), labels=vegetables)

# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
         rotation_mode="anchor")

# Loop over data dimensions and create text annotations.
for i in range(len(vegetables)):
    for j in range(len(farmers)):
        text = ax.text(j, i, harvest[i, j],
                       ha="center", va="center", color="w")

ax.set_title("Harvest of local farmers (in tons/year)")
fig.tight_layout()
plt.show()

Code: Alles auswählen

mport os
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
 
zip_file = keras.utils.get_file(
    fname="cora.tgz",
    origin="https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz",
    extract=True,
)
data_dir = os.path.join(os.path.dirname(zip_file), "cora")
#Process and visualize the dataset
#Then we load the citations data into a Pandas DataFrame.

citations = pd.read_csv(
    os.path.join(data_dir, "cora.cites"),
    sep="\t",
    header=None,
    names=["target", "source"],
)
print("Citations shape:", citations.shape)
#Citations shape: (5429, 2)
#Now we display a sample of the citations DataFrame. The target column includes the paper ids cited by the paper ids in the source column.

citations.sample(frac=1).head()
#pers data into a Pandas DataFrame.

column_names = ["paper_id"] + [f"term_{idx}" for idx in range(1433)] + ["subject"]
papers = pd.read_csv(
    os.path.join(data_dir, "cora.content"), sep="\t", header=None, names=column_names,
)
print("Papers shape:", papers.shape)
print(papers.sample(5).T)
print(papers.subject.value_counts())
 
class_values = sorted(papers["subject"].unique())
class_idx = {name: id for id, name in enumerate(class_values)}
paper_idx = {name: idx for idx, name in enumerate(sorted(papers["paper_id"].unique()))}

papers["paper_id"] = papers["paper_id"].apply(lambda name: paper_idx[name])
citations["source"] = citations["source"].apply(lambda name: paper_idx[name])
citations["target"] = citations["target"].apply(lambda name: paper_idx[name])
papers["subject"] = papers["subject"].apply(lambda value: class_idx[value])
#Now let's visualize the citation graph. Each node in the graph represents a paper, and the color of the node corresponds to its subject. Note that we only show a sample of the papers in the dataset.
plt.figure(figsize=(10, 10))
colors = papers["subject"].tolist()
cora_graph = nx.from_pandas_edgelist(citations.sample(n=1500))
subjects = list(papers[papers["paper_id"].isin(list(cora_graph.nodes))]["subject"])
nx.draw_spring(cora_graph, node_size=15, node_color=subjects)
plt.show() 

Code: Alles auswählen

 
                    #                   Die  funktionale API   von Keras    kann Modelle erstellen,
                    #                    die flexibler        sind als die             keras.SequentialAPI.
                    #                   Die funktionale   API kann Modelle mit nichtlinearer Topologie,
                    #                   gemeinsam genutzte   Ebenen und sogar mehrere
                    #                   Ein- oder Ausgaben verarbeiten.



import numpy as np
import keras
from keras import layers
from keras import ops

#Eingabeknoten
inputs = keras.Input(shape=(784,))


#  zur Deminstration
img_inputs = keras.Input(shape=(32, 32, 3))

# Form und Datentyp
inputs.shape
inputs.dtype

#Aufruf einer Knotenebene
dense = layers.Dense(64, activation="relu")
x = dense(inputs)
# noch ein paar weitere Ebenen  
x = layers.Dense(64, activation="relu")(x)
outputs = layers.Dense(10)(x)
model = keras.Model(inputs=inputs, outputs=outputs, name="mnist_model")
#Schauen wir uns an, wie die Modellzusammenfassung aussieht:
print(model.summary())