将梯度可视化为Tensorflow 2中的热图

我正在进行一项任务，通过引导反向传播生成热图。我已经覆盖了原始的Relu，并获得了每个参数的梯度。不过，我不知道下一步该怎么办。感谢您的帮助！谢谢大家!

这是我的密码：

我首先使用

@tf.registergradent（“GuidedRelu”）

如下：

def _GuidedReluGrad(op, grad):
    gate_f = tf.cast(op.outputs[0] > 0, "float32")
    gate_R = tf.cast(grad > 0, "float32")
    return gate_f * gate_R * grad

然后，我通过以下方式获得了学位：

with g.gradient_override_map({"Relu": "GuidedRelu"}):
    with tf.GradientTape() as tape:
        logits = self.net(tf.cast(img, dtype=tf.float32))
        xentropy = tf.nn.softmax_cross_entropy_with_logits(
            labels=tf.cast(
                tf.one_hot(predicted_class, depth=1000), dtype=tf.int32
            ),
            logits=logits,
        )
        reduced = tf.reduce_mean(xentropy)
        grads = tape.gradient(reduced, self.net.trainable_variables)

---

我发现第一层的梯度有形状（7,7,3,64）。但是我不知道如何使用这个梯度来生成与输入大小相似的热图。

它类似于层的内核可视化。下面是一个示例，我正在可视化具有

（7,7,4,4）

形状的

Conv2D

层

（7,7,4,4）

表示该层具有

7*7

内核

和

4

传入过滤器（前一层的过滤器），最后一个

4

是该层的

传出过滤器

所以在你的例子中，

（7,7,3,64）

意味着你有

7*7

内核

和

3

传入过滤器

（因为它是你的第一层，猜测你的输入是彩色图像），

64

是你的层

过滤器

为供参考，我已经打印了我模型的所有卷积层。我使用可视化代码中的相同代码来获得最后一层的过滤器形状

conv2d_3（7,7,4,4）

并将其用于可视化-

# summarize filter shapes
for layer in model.layers:
    # check for convolutional layer
    if 'conv' in layer.name:
      # get filter weights
      filters, biases = layer.get_weights()
      print(layer.name, filters.shape)

输出-

conv2d_1 (3, 3, 3, 2)
conv2d_2 (3, 3, 2, 4)
conv2d_3 (7, 7, 4, 4)

# (1) Importing dependency
%tensorflow_version 1.x
import tensorflow as tf
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, Conv3D
from keras.layers.normalization import BatchNormalization
import numpy as np

np.random.seed(1000)

# (2) Get Data
import tflearn.datasets.oxflower17 as oxflower17
x, y = oxflower17.load_data(one_hot=True)

# (3) Create a sequential model
model = Sequential()

# 1st Convolutional Layer
model.add(Conv2D(filters=2, input_shape=(224,224,3), kernel_size=(3,3), strides=(4,4), padding='Same'))
model.add(Activation('relu'))

# 2nd Convolutional Layer
model.add(Conv2D(filters=4, kernel_size=(3,3), strides=(1,1), padding='Same'))
model.add(Activation('relu'))

# 3rd Convolutional Layer
model.add(Conv2D(filters=4, kernel_size=(7,7), strides=(1,1), padding='Same'))
model.add(Activation('relu'))

# Passing it to a dense layer
model.add(Flatten())
# 1st Dense Layer
model.add(Dense(100))
model.add(Activation('relu'))

# Output Layer
model.add(Dense(17))
model.add(Activation('softmax'))

model.summary()

# (4) Compile 
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
epoch_gradient = []

def get_gradient_func(model):
    grads = K.gradients(model.total_loss, model.trainable_weights)
    inputs = model.model._feed_inputs + model.model._feed_targets + model.model._feed_sample_weights
    func = K.function(inputs, grads)
    return func

# Define the Required Callback Function
class GradientCalcCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs=None):
      get_gradient = get_gradient_func(model)
      grads = get_gradient([x, y, np.ones(len(y))])
      epoch_gradient.append(grads)

epoch = 4

model.fit(x, y, batch_size=64, epochs= epoch, verbose=1, validation_split=0.2, shuffle=True, callbacks=[GradientCalcCallback()])

# (7) Convert to a 2 dimensiaonal array of (epoch, gradients) type
gradient = np.asarray(epoch_gradient)
print("Total number of epochs run:", epoch)
print("Gradient Array has the shape:",gradient.shape)

TensorFlow 1.x selected.
Using TensorFlow backend.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/summarizer.py:9: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/trainer.py:25: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/collections.py:13: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:123: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:129: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:131: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.

Downloading Oxford 17 category Flower Dataset, Please wait...
100.0% 60276736 / 60270631
('Succesfully downloaded', '17flowers.tgz', 60270631, 'bytes.')
File Extracted
Starting to parse images...
Parsing Done!
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 56, 56, 2)         56        
_________________________________________________________________
activation_1 (Activation)    (None, 56, 56, 2)         0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 56, 56, 4)         76        
_________________________________________________________________
activation_2 (Activation)    (None, 56, 56, 4)         0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 56, 56, 4)         788       
_________________________________________________________________
activation_3 (Activation)    (None, 56, 56, 4)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 12544)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 100)               1254500   
_________________________________________________________________
activation_4 (Activation)    (None, 100)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 17)                1717      
_________________________________________________________________
activation_5 (Activation)    (None, 17)                0         
=================================================================
Total params: 1,257,137
Trainable params: 1,257,137
Non-trainable params: 0
_________________________________________________________________
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:431: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:438: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.

Train on 1088 samples, validate on 272 samples
Epoch 1/4
1088/1088 [==============================] - 5s 5ms/step - loss: 2.8055 - accuracy: 0.0846 - val_loss: 2.7566 - val_accuracy: 0.1176
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 2/4
1088/1088 [==============================] - 5s 5ms/step - loss: 2.3974 - accuracy: 0.3263 - val_loss: 2.5707 - val_accuracy: 0.2132
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 3/4
1088/1088 [==============================] - 5s 5ms/step - loss: 1.5953 - accuracy: 0.5506 - val_loss: 2.4076 - val_accuracy: 0.2684
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 4/4
1088/1088 [==============================] - 5s 5ms/step - loss: 0.8699 - accuracy: 0.7812 - val_loss: 2.5698 - val_accuracy: 0.3162
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Total number of epochs run: 4
Gradient Array has the shape: (4, 10)

我们将可视化conv2d_3（7,7,4,4），因为它与您的要求类似。所以基本上我们应该有（

incoming filters*outing filters

=

16

）16个大小为

7*7

的图像

可视化代码-您需要修改传入的\u过滤器和传出的\u过滤器，它们分别是前一层的过滤器（如果是第一层，则是图像的通道大小）和该层的过滤器

from matplotlib import pyplot

# filters will have details of last Conv layer .i.e. conv2d_3 (7, 7, 4, 4)
for layer in model.layers:
    # check for convolutional layer
    if 'conv' in layer.name:
      # get filter weights
      filters, biases = layer.get_weights()

# Fix the figure size
fig, ax = pyplot.subplots(figsize=(15, 15))

# Normalize filter values to 0-1 so we can visualize them
f_min, f_max = filters.min(), filters.max()
filters = (filters - f_min) / (f_max - f_min)
outgoing_filters, ix = 4, 1 
for i in range(outgoing_filters):
    # get the filter
    f = filters[:, :, :, i]
    # plot each channel separately
    incoming_filters = 4 
    for j in range(incoming_filters):
        # specify subplot and turn of axis
        ax = pyplot.subplot(incoming_filters, outgoing_filters, ix)
        ax.set_xticks([])
        ax.set_yticks([])
        # plot filter channel 
        # Use cmap='gray' for Gray scale image
        pyplot.imshow(f[:, :, j]) 
        ix += 1

# show the figure
pyplot.show()

输出-

conv2d_1 (3, 3, 3, 2)
conv2d_2 (3, 3, 2, 4)
conv2d_3 (7, 7, 4, 4)

# (1) Importing dependency
%tensorflow_version 1.x
import tensorflow as tf
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, Conv3D
from keras.layers.normalization import BatchNormalization
import numpy as np

np.random.seed(1000)

# (2) Get Data
import tflearn.datasets.oxflower17 as oxflower17
x, y = oxflower17.load_data(one_hot=True)

# (3) Create a sequential model
model = Sequential()

# 1st Convolutional Layer
model.add(Conv2D(filters=2, input_shape=(224,224,3), kernel_size=(3,3), strides=(4,4), padding='Same'))
model.add(Activation('relu'))

# 2nd Convolutional Layer
model.add(Conv2D(filters=4, kernel_size=(3,3), strides=(1,1), padding='Same'))
model.add(Activation('relu'))

# 3rd Convolutional Layer
model.add(Conv2D(filters=4, kernel_size=(7,7), strides=(1,1), padding='Same'))
model.add(Activation('relu'))

# Passing it to a dense layer
model.add(Flatten())
# 1st Dense Layer
model.add(Dense(100))
model.add(Activation('relu'))

# Output Layer
model.add(Dense(17))
model.add(Activation('softmax'))

model.summary()

# (4) Compile 
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
epoch_gradient = []

def get_gradient_func(model):
    grads = K.gradients(model.total_loss, model.trainable_weights)
    inputs = model.model._feed_inputs + model.model._feed_targets + model.model._feed_sample_weights
    func = K.function(inputs, grads)
    return func

# Define the Required Callback Function
class GradientCalcCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs=None):
      get_gradient = get_gradient_func(model)
      grads = get_gradient([x, y, np.ones(len(y))])
      epoch_gradient.append(grads)

epoch = 4

model.fit(x, y, batch_size=64, epochs= epoch, verbose=1, validation_split=0.2, shuffle=True, callbacks=[GradientCalcCallback()])

# (7) Convert to a 2 dimensiaonal array of (epoch, gradients) type
gradient = np.asarray(epoch_gradient)
print("Total number of epochs run:", epoch)
print("Gradient Array has the shape:",gradient.shape)

TensorFlow 1.x selected.
Using TensorFlow backend.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/summarizer.py:9: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/trainer.py:25: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/collections.py:13: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:123: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:129: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:131: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.

Downloading Oxford 17 category Flower Dataset, Please wait...
100.0% 60276736 / 60270631
('Succesfully downloaded', '17flowers.tgz', 60270631, 'bytes.')
File Extracted
Starting to parse images...
Parsing Done!
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 56, 56, 2)         56        
_________________________________________________________________
activation_1 (Activation)    (None, 56, 56, 2)         0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 56, 56, 4)         76        
_________________________________________________________________
activation_2 (Activation)    (None, 56, 56, 4)         0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 56, 56, 4)         788       
_________________________________________________________________
activation_3 (Activation)    (None, 56, 56, 4)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 12544)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 100)               1254500   
_________________________________________________________________
activation_4 (Activation)    (None, 100)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 17)                1717      
_________________________________________________________________
activation_5 (Activation)    (None, 17)                0         
=================================================================
Total params: 1,257,137
Trainable params: 1,257,137
Non-trainable params: 0
_________________________________________________________________
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:431: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:438: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.

Train on 1088 samples, validate on 272 samples
Epoch 1/4
1088/1088 [==============================] - 5s 5ms/step - loss: 2.8055 - accuracy: 0.0846 - val_loss: 2.7566 - val_accuracy: 0.1176
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 2/4
1088/1088 [==============================] - 5s 5ms/step - loss: 2.3974 - accuracy: 0.3263 - val_loss: 2.5707 - val_accuracy: 0.2132
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 3/4
1088/1088 [==============================] - 5s 5ms/step - loss: 1.5953 - accuracy: 0.5506 - val_loss: 2.4076 - val_accuracy: 0.2684
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 4/4
1088/1088 [==============================] - 5s 5ms/step - loss: 0.8699 - accuracy: 0.7812 - val_loss: 2.5698 - val_accuracy: 0.3162
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Total number of epochs run: 4
Gradient Array has the shape: (4, 10)

希望这能回答你的问题。快乐学习

---

编辑-在每一个历元之后都会更加努力地捕捉渐变并将其可视化。下面的代码用于捕获每个历元后的渐变。我使用旧方法在Tensorflow 1.15.0中捕获渐变，而不是使用

tf.GradientTape

。如果您想知道如何使用

tf.GradientTape

捕获梯度，您可以参考我们的答案

在下面的程序中，

gradient

是

array

，它在每个层的每个历元之后都捕获了梯度

代码-

conv2d_1 (3, 3, 3, 2)
conv2d_2 (3, 3, 2, 4)
conv2d_3 (7, 7, 4, 4)

# (1) Importing dependency
%tensorflow_version 1.x
import tensorflow as tf
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, Conv3D
from keras.layers.normalization import BatchNormalization
import numpy as np

np.random.seed(1000)

# (2) Get Data
import tflearn.datasets.oxflower17 as oxflower17
x, y = oxflower17.load_data(one_hot=True)

# (3) Create a sequential model
model = Sequential()

# 1st Convolutional Layer
model.add(Conv2D(filters=2, input_shape=(224,224,3), kernel_size=(3,3), strides=(4,4), padding='Same'))
model.add(Activation('relu'))

# 2nd Convolutional Layer
model.add(Conv2D(filters=4, kernel_size=(3,3), strides=(1,1), padding='Same'))
model.add(Activation('relu'))

# 3rd Convolutional Layer
model.add(Conv2D(filters=4, kernel_size=(7,7), strides=(1,1), padding='Same'))
model.add(Activation('relu'))

# Passing it to a dense layer
model.add(Flatten())
# 1st Dense Layer
model.add(Dense(100))
model.add(Activation('relu'))

# Output Layer
model.add(Dense(17))
model.add(Activation('softmax'))

model.summary()

# (4) Compile 
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
epoch_gradient = []

def get_gradient_func(model):
    grads = K.gradients(model.total_loss, model.trainable_weights)
    inputs = model.model._feed_inputs + model.model._feed_targets + model.model._feed_sample_weights
    func = K.function(inputs, grads)
    return func

# Define the Required Callback Function
class GradientCalcCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs=None):
      get_gradient = get_gradient_func(model)
      grads = get_gradient([x, y, np.ones(len(y))])
      epoch_gradient.append(grads)

epoch = 4

model.fit(x, y, batch_size=64, epochs= epoch, verbose=1, validation_split=0.2, shuffle=True, callbacks=[GradientCalcCallback()])

# (7) Convert to a 2 dimensiaonal array of (epoch, gradients) type
gradient = np.asarray(epoch_gradient)
print("Total number of epochs run:", epoch)
print("Gradient Array has the shape:",gradient.shape)

TensorFlow 1.x selected.
Using TensorFlow backend.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/summarizer.py:9: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/trainer.py:25: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/collections.py:13: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:123: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:129: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:131: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.

Downloading Oxford 17 category Flower Dataset, Please wait...
100.0% 60276736 / 60270631
('Succesfully downloaded', '17flowers.tgz', 60270631, 'bytes.')
File Extracted
Starting to parse images...
Parsing Done!
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 56, 56, 2)         56        
_________________________________________________________________
activation_1 (Activation)    (None, 56, 56, 2)         0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 56, 56, 4)         76        
_________________________________________________________________
activation_2 (Activation)    (None, 56, 56, 4)         0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 56, 56, 4)         788       
_________________________________________________________________
activation_3 (Activation)    (None, 56, 56, 4)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 12544)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 100)               1254500   
_________________________________________________________________
activation_4 (Activation)    (None, 100)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 17)                1717      
_________________________________________________________________
activation_5 (Activation)    (None, 17)                0         
=================================================================
Total params: 1,257,137
Trainable params: 1,257,137
Non-trainable params: 0
_________________________________________________________________
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:431: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:438: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.

Train on 1088 samples, validate on 272 samples
Epoch 1/4
1088/1088 [==============================] - 5s 5ms/step - loss: 2.8055 - accuracy: 0.0846 - val_loss: 2.7566 - val_accuracy: 0.1176
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 2/4
1088/1088 [==============================] - 5s 5ms/step - loss: 2.3974 - accuracy: 0.3263 - val_loss: 2.5707 - val_accuracy: 0.2132
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 3/4
1088/1088 [==============================] - 5s 5ms/step - loss: 1.5953 - accuracy: 0.5506 - val_loss: 2.4076 - val_accuracy: 0.2684
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 4/4
1088/1088 [==============================] - 5s 5ms/step - loss: 0.8699 - accuracy: 0.7812 - val_loss: 2.5698 - val_accuracy: 0.3162
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Total number of epochs run: 4
Gradient Array has the shape: (4, 10)

输出-

conv2d_1 (3, 3, 3, 2)
conv2d_2 (3, 3, 2, 4)
conv2d_3 (7, 7, 4, 4)

# (1) Importing dependency
%tensorflow_version 1.x
import tensorflow as tf
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, Conv3D
from keras.layers.normalization import BatchNormalization
import numpy as np

np.random.seed(1000)

# (2) Get Data
import tflearn.datasets.oxflower17 as oxflower17
x, y = oxflower17.load_data(one_hot=True)

# (3) Create a sequential model
model = Sequential()

# 1st Convolutional Layer
model.add(Conv2D(filters=2, input_shape=(224,224,3), kernel_size=(3,3), strides=(4,4), padding='Same'))
model.add(Activation('relu'))

# 2nd Convolutional Layer
model.add(Conv2D(filters=4, kernel_size=(3,3), strides=(1,1), padding='Same'))
model.add(Activation('relu'))

# 3rd Convolutional Layer
model.add(Conv2D(filters=4, kernel_size=(7,7), strides=(1,1), padding='Same'))
model.add(Activation('relu'))

# Passing it to a dense layer
model.add(Flatten())
# 1st Dense Layer
model.add(Dense(100))
model.add(Activation('relu'))

# Output Layer
model.add(Dense(17))
model.add(Activation('softmax'))

model.summary()

# (4) Compile 
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
epoch_gradient = []

def get_gradient_func(model):
    grads = K.gradients(model.total_loss, model.trainable_weights)
    inputs = model.model._feed_inputs + model.model._feed_targets + model.model._feed_sample_weights
    func = K.function(inputs, grads)
    return func

# Define the Required Callback Function
class GradientCalcCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs=None):
      get_gradient = get_gradient_func(model)
      grads = get_gradient([x, y, np.ones(len(y))])
      epoch_gradient.append(grads)

epoch = 4

model.fit(x, y, batch_size=64, epochs= epoch, verbose=1, validation_split=0.2, shuffle=True, callbacks=[GradientCalcCallback()])

# (7) Convert to a 2 dimensiaonal array of (epoch, gradients) type
gradient = np.asarray(epoch_gradient)
print("Total number of epochs run:", epoch)
print("Gradient Array has the shape:",gradient.shape)

TensorFlow 1.x selected.
Using TensorFlow backend.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/summarizer.py:9: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/trainer.py:25: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/collections.py:13: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:123: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:129: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:131: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.

Downloading Oxford 17 category Flower Dataset, Please wait...
100.0% 60276736 / 60270631
('Succesfully downloaded', '17flowers.tgz', 60270631, 'bytes.')
File Extracted
Starting to parse images...
Parsing Done!
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 56, 56, 2)         56        
_________________________________________________________________
activation_1 (Activation)    (None, 56, 56, 2)         0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 56, 56, 4)         76        
_________________________________________________________________
activation_2 (Activation)    (None, 56, 56, 4)         0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 56, 56, 4)         788       
_________________________________________________________________
activation_3 (Activation)    (None, 56, 56, 4)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 12544)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 100)               1254500   
_________________________________________________________________
activation_4 (Activation)    (None, 100)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 17)                1717      
_________________________________________________________________
activation_5 (Activation)    (None, 17)                0         
=================================================================
Total params: 1,257,137
Trainable params: 1,257,137
Non-trainable params: 0
_________________________________________________________________
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:431: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:438: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.

Train on 1088 samples, validate on 272 samples
Epoch 1/4
1088/1088 [==============================] - 5s 5ms/step - loss: 2.8055 - accuracy: 0.0846 - val_loss: 2.7566 - val_accuracy: 0.1176
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 2/4
1088/1088 [==============================] - 5s 5ms/step - loss: 2.3974 - accuracy: 0.3263 - val_loss: 2.5707 - val_accuracy: 0.2132
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 3/4
1088/1088 [==============================] - 5s 5ms/step - loss: 1.5953 - accuracy: 0.5506 - val_loss: 2.4076 - val_accuracy: 0.2684
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 4/4
1088/1088 [==============================] - 5s 5ms/step - loss: 0.8699 - accuracy: 0.7812 - val_loss: 2.5698 - val_accuracy: 0.3162
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Total number of epochs run: 4
Gradient Array has the shape: (4, 10)

可视化-

conv2d_1 (3, 3, 3, 2)
conv2d_2 (3, 3, 2, 4)
conv2d_3 (7, 7, 4, 4)

# (1) Importing dependency
%tensorflow_version 1.x
import tensorflow as tf
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, Conv3D
from keras.layers.normalization import BatchNormalization
import numpy as np

np.random.seed(1000)

# (2) Get Data
import tflearn.datasets.oxflower17 as oxflower17
x, y = oxflower17.load_data(one_hot=True)

# (3) Create a sequential model
model = Sequential()

# 1st Convolutional Layer
model.add(Conv2D(filters=2, input_shape=(224,224,3), kernel_size=(3,3), strides=(4,4), padding='Same'))
model.add(Activation('relu'))

# 2nd Convolutional Layer
model.add(Conv2D(filters=4, kernel_size=(3,3), strides=(1,1), padding='Same'))
model.add(Activation('relu'))

# 3rd Convolutional Layer
model.add(Conv2D(filters=4, kernel_size=(7,7), strides=(1,1), padding='Same'))
model.add(Activation('relu'))

# Passing it to a dense layer
model.add(Flatten())
# 1st Dense Layer
model.add(Dense(100))
model.add(Activation('relu'))

# Output Layer
model.add(Dense(17))
model.add(Activation('softmax'))

model.summary()

# (4) Compile 
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
epoch_gradient = []

def get_gradient_func(model):
    grads = K.gradients(model.total_loss, model.trainable_weights)
    inputs = model.model._feed_inputs + model.model._feed_targets + model.model._feed_sample_weights
    func = K.function(inputs, grads)
    return func

# Define the Required Callback Function
class GradientCalcCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs=None):
      get_gradient = get_gradient_func(model)
      grads = get_gradient([x, y, np.ones(len(y))])
      epoch_gradient.append(grads)

epoch = 4

model.fit(x, y, batch_size=64, epochs= epoch, verbose=1, validation_split=0.2, shuffle=True, callbacks=[GradientCalcCallback()])

# (7) Convert to a 2 dimensiaonal array of (epoch, gradients) type
gradient = np.asarray(epoch_gradient)
print("Total number of epochs run:", epoch)
print("Gradient Array has the shape:",gradient.shape)

TensorFlow 1.x selected.
Using TensorFlow backend.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/summarizer.py:9: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/trainer.py:25: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/collections.py:13: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:123: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:129: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:131: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.

Downloading Oxford 17 category Flower Dataset, Please wait...
100.0% 60276736 / 60270631
('Succesfully downloaded', '17flowers.tgz', 60270631, 'bytes.')
File Extracted
Starting to parse images...
Parsing Done!
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 56, 56, 2)         56        
_________________________________________________________________
activation_1 (Activation)    (None, 56, 56, 2)         0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 56, 56, 4)         76        
_________________________________________________________________
activation_2 (Activation)    (None, 56, 56, 4)         0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 56, 56, 4)         788       
_________________________________________________________________
activation_3 (Activation)    (None, 56, 56, 4)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 12544)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 100)               1254500   
_________________________________________________________________
activation_4 (Activation)    (None, 100)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 17)                1717      
_________________________________________________________________
activation_5 (Activation)    (None, 17)                0         
=================================================================
Total params: 1,257,137
Trainable params: 1,257,137
Non-trainable params: 0
_________________________________________________________________
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:431: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:438: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.

Train on 1088 samples, validate on 272 samples
Epoch 1/4
1088/1088 [==============================] - 5s 5ms/step - loss: 2.8055 - accuracy: 0.0846 - val_loss: 2.7566 - val_accuracy: 0.1176
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 2/4
1088/1088 [==============================] - 5s 5ms/step - loss: 2.3974 - accuracy: 0.3263 - val_loss: 2.5707 - val_accuracy: 0.2132
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 3/4
1088/1088 [==============================] - 5s 5ms/step - loss: 1.5953 - accuracy: 0.5506 - val_loss: 2.4076 - val_accuracy: 0.2684
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 4/4
1088/1088 [==============================] - 5s 5ms/step - loss: 0.8699 - accuracy: 0.7812 - val_loss: 2.5698 - val_accuracy: 0.3162
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Total number of epochs run: 4
Gradient Array has the shape: (4, 10)

让我们可视化

渐变[0][4]

，即<代码>[0]表示模型的第一个纪元，

[4]

表示模型的第五个纪元

from matplotlib import pyplot

filters = gradient[0][4]

# Fix the figure size
fig, ax = pyplot.subplots(figsize=(15, 15))

# Normalize filter values to 0-1 so we can visualize them
f_min, f_max = filters.min(), filters.max()
filters = (filters - f_min) / (f_max - f_min)
outgoing_filters, ix = 4, 1 
for i in range(outgoing_filters):
    # get the filter
    f = filters[:, :, :, i]
    # plot each channel separately
    incoming_filters = 4 
    for j in range(incoming_filters):
        # specify subplot and turn of axis
        ax = pyplot.subplot(incoming_filters, outgoing_filters, ix)
        ax.set_xticks([])
        ax.set_yticks([])
        # plot filter channel 
        # Use cmap='gray' for Gray scale image
        pyplot.imshow(f[:, :, j]) 
        ix += 1

# show the figure
pyplot.show()

输出-

conv2d_1 (3, 3, 3, 2)
conv2d_2 (3, 3, 2, 4)
conv2d_3 (7, 7, 4, 4)

# (1) Importing dependency
%tensorflow_version 1.x
import tensorflow as tf
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, Conv3D
from keras.layers.normalization import BatchNormalization
import numpy as np

np.random.seed(1000)

# (2) Get Data
import tflearn.datasets.oxflower17 as oxflower17
x, y = oxflower17.load_data(one_hot=True)

# (3) Create a sequential model
model = Sequential()

# 1st Convolutional Layer
model.add(Conv2D(filters=2, input_shape=(224,224,3), kernel_size=(3,3), strides=(4,4), padding='Same'))
model.add(Activation('relu'))

# 2nd Convolutional Layer
model.add(Conv2D(filters=4, kernel_size=(3,3), strides=(1,1), padding='Same'))
model.add(Activation('relu'))

# 3rd Convolutional Layer
model.add(Conv2D(filters=4, kernel_size=(7,7), strides=(1,1), padding='Same'))
model.add(Activation('relu'))

# Passing it to a dense layer
model.add(Flatten())
# 1st Dense Layer
model.add(Dense(100))
model.add(Activation('relu'))

# Output Layer
model.add(Dense(17))
model.add(Activation('softmax'))

model.summary()

# (4) Compile 
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
epoch_gradient = []

def get_gradient_func(model):
    grads = K.gradients(model.total_loss, model.trainable_weights)
    inputs = model.model._feed_inputs + model.model._feed_targets + model.model._feed_sample_weights
    func = K.function(inputs, grads)
    return func

# Define the Required Callback Function
class GradientCalcCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs=None):
      get_gradient = get_gradient_func(model)
      grads = get_gradient([x, y, np.ones(len(y))])
      epoch_gradient.append(grads)

epoch = 4

model.fit(x, y, batch_size=64, epochs= epoch, verbose=1, validation_split=0.2, shuffle=True, callbacks=[GradientCalcCallback()])

# (7) Convert to a 2 dimensiaonal array of (epoch, gradients) type
gradient = np.asarray(epoch_gradient)
print("Total number of epochs run:", epoch)
print("Gradient Array has the shape:",gradient.shape)

TensorFlow 1.x selected.
Using TensorFlow backend.
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/summarizer.py:9: The name tf.summary.merge is deprecated. Please use tf.compat.v1.summary.merge instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/helpers/trainer.py:25: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/collections.py:13: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:123: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:129: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead.

WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tflearn/config.py:131: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.

Downloading Oxford 17 category Flower Dataset, Please wait...
100.0% 60276736 / 60270631
('Succesfully downloaded', '17flowers.tgz', 60270631, 'bytes.')
File Extracted
Starting to parse images...
Parsing Done!
WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 56, 56, 2)         56        
_________________________________________________________________
activation_1 (Activation)    (None, 56, 56, 2)         0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 56, 56, 4)         76        
_________________________________________________________________
activation_2 (Activation)    (None, 56, 56, 4)         0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 56, 56, 4)         788       
_________________________________________________________________
activation_3 (Activation)    (None, 56, 56, 4)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 12544)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 100)               1254500   
_________________________________________________________________
activation_4 (Activation)    (None, 100)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 17)                1717      
_________________________________________________________________
activation_5 (Activation)    (None, 17)                0         
=================================================================
Total params: 1,257,137
Trainable params: 1,257,137
Non-trainable params: 0
_________________________________________________________________
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:431: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:438: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.

Train on 1088 samples, validate on 272 samples
Epoch 1/4
1088/1088 [==============================] - 5s 5ms/step - loss: 2.8055 - accuracy: 0.0846 - val_loss: 2.7566 - val_accuracy: 0.1176
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 2/4
1088/1088 [==============================] - 5s 5ms/step - loss: 2.3974 - accuracy: 0.3263 - val_loss: 2.5707 - val_accuracy: 0.2132
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 3/4
1088/1088 [==============================] - 5s 5ms/step - loss: 1.5953 - accuracy: 0.5506 - val_loss: 2.4076 - val_accuracy: 0.2684
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Epoch 4/4
1088/1088 [==============================] - 5s 5ms/step - loss: 0.8699 - accuracy: 0.7812 - val_loss: 2.5698 - val_accuracy: 0.3162
/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py:111: UserWarning: `Sequential.model` is deprecated. `Sequential` is a subclass of `Model`, you can just use your `Sequential` instance directly.
  warnings.warn('`Sequential.model` is deprecated. '
Total number of epochs run: 4
Gradient Array has the shape: (4, 10)

如果您想可视化

Conv3D

，请参考此

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希望这能详细回答你的问题。快乐学习。

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