Numpy实现深度学习Model _实现

最是人间留不住，朱颜辞镜花辞树。这篇文章主要讲述Numpy实现深度学习Model相关的知识，希望能为你提供帮助。

from __future__ import print_function, division
from terminaltables import AsciiTable
import numpy as np
import progressbar
from mlfromscratch.utils import batch_iterator
from mlfromscratch.utils.misc import bar_widgets

class NeuralNetwork():
"""Neural Network. Deep Learning base model.

Parameters:
-----------
optimizer: class
The weight optimizer that will be used to tune the weights in order of minimizing
the loss.
loss: class
Loss function used to measure the model\'s performance. SquareLoss or CrossEntropy.
validation: tuple
A tuple containing validation data and labels (X, y)
"""
def __init__(self, optimizer, loss, validation_data=https://www.songbingjia.com/android/None):
self.optimizer = optimizer
self.layers = []
self.errors = {"training": [], "validation": []}
self.loss_function = loss()
self.progressbar = progressbar.ProgressBar(widgets=bar_widgets)

self.val_set = None
if validation_data:
X, y = validation_data
self.val_set = {"X": X, "y": y}

def set_trainable(self, trainable):
""" Method which enables freezing of the weights of the network\'s layers. """
for layer in self.layers:
layer.trainable = trainable

def add(self, layer):
""" Method which adds a layer to the neural network """
# If this is not the first layer added then set the input shape
# to the output shape of the last added layer
if self.layers:
layer.set_input_shape(shape=self.layers[-1].output_shape())

# If the layer has weights that needs to be initialized
if hasattr(layer, \'initialize\'):
layer.initialize(optimizer=self.optimizer)

# Add layer to the network
self.layers.append(layer)

def test_on_batch(self, X, y):
""" Evaluates the model over a single batch of samples """
y_pred = self._forward_pass(X, training=False)
loss = np.mean(self.loss_function.loss(y, y_pred))
acc = self.loss_function.acc(y, y_pred)

return loss, acc

def train_on_batch(self, X, y):
""" Single gradient update over one batch of samples """
y_pred = self._forward_pass(X)
loss = np.mean(self.loss_function.loss(y, y_pred))
acc = self.loss_function.acc(y, y_pred)
# Calculate the gradient of the loss function wrt y_pred
loss_grad = self.loss_function.gradient(y, y_pred)
# Backpropagate. Update weights
self._backward_pass(loss_grad=loss_grad)

return loss, acc

def fit(self, X, y, n_epochs, batch_size):
""" Trains the model for a fixed number of epochs """
for _ in self.progressbar(range(n_epochs)):

batch_error = []
for X_batch, y_batch in batch_iterator(X, y, batch_size=batch_size):
loss, _ = self.train_on_batch(X_batch, y_batch)
batch_error.append(loss)

self.errors["training"].append(np.mean(batch_error))

if self.val_set is not None:
val_loss, _ = self.test_on_batch(self.val_set["X"], self.val_set["y"])
self.errors["validation"].append(val_loss)

return self.errors["training"], self.errors["validation"]

def _forward_pass(self, X, training=True):
""" Calculate the output of the NN """
layer_output = X
for layer in self.layers:
layer_output = layer.forward_pass(layer_output, training)

return layer_output

def _backward_pass(self, loss_grad):
""" Propagate the gradient \'backwards\' and update the weights in each layer """
for layer in reversed(self.layers):
loss_grad = layer.backward_pass(loss_grad)

def summary(self, name="Model Summary"):
# Print model name
print (AsciiTable([[name]]).table)
# Network input shape (first layer\'s input shape)
print ("Input Shape: %s" % str(self.layers[0].input_shape))
# Iterate through network and get each layer\'s configuration
table_data = https://www.songbingjia.com/android/[["Layer Type", "Parameters", "Output Shape"]]
tot_params = 0
for layer in self.layers:
layer_name = layer.layer_name()
params = layer.parameters()
out_shape = layer.output_shape()
table_data.append([layer_name, str(params), str(out_shape)])
tot_params += params
# Print network configuration table
print (AsciiTable(table_data).table)
print ("Total Parameters: %d\\n" % tot_params)

def predict(self, X):
""" Use the trained model to predict labels of X """
return self._forward_pass(X, training=False)