caffe
पाइकफ के साथ एक कैफ मॉडल का प्रशिक्षण

आइरिस डेटासेट पर एक नेटवर्क का प्रशिक्षण

नीचे दिया गया एक सरल उदाहरण है पाइकॉन का उपयोग करते हुए पायथन में आइरिस डेटा पर कैफ मॉडल को प्रशिक्षित करना। यह कुछ उपयोगकर्ता-परिभाषित इनपुटों को देखते हुए अनुमानित आउटपुट भी देता है।

iris_tuto.py

import subprocess
import platform
import copy

from sklearn.datasets import load_iris
import sklearn.metrics 
import numpy as np
from sklearn.cross_validation import StratifiedShuffleSplit
import matplotlib.pyplot as plt
import h5py
import caffe
import caffe.draw


def load_data():
    '''
    Load Iris Data set
    '''
    data = load_iris()
    print(data.data)
    print(data.target)
    targets = np.zeros((len(data.target), 3))
    for count, target in enumerate(data.target):
        targets[count][target]= 1    
    print(targets)

    new_data = {}
    #new_data['input'] = data.data
    new_data['input'] = np.reshape(data.data, (150,1,1,4))
    new_data['output'] = targets
    #print(new_data['input'].shape)
    #new_data['input'] = np.random.random((150, 1, 1, 4))
    #print(new_data['input'].shape)   
    #new_data['output'] = np.random.random_integers(0, 1, size=(150,3))    
    #print(new_data['input'])

    return new_data

def save_data_as_hdf5(hdf5_data_filename, data):
    '''
    HDF5 is one of the data formats Caffe accepts
    '''
    with h5py.File(hdf5_data_filename, 'w') as f:
        f['data'] = data['input'].astype(np.float32)
        f['label'] = data['output'].astype(np.float32)


def train(solver_prototxt_filename):
    '''
    Train the ANN
    '''
    caffe.set_mode_cpu()
    solver = caffe.get_solver(solver_prototxt_filename)
    solver.solve()


def print_network_parameters(net):
    '''
    Print the parameters of the network
    '''
    print(net)
    print('net.inputs: {0}'.format(net.inputs))
    print('net.outputs: {0}'.format(net.outputs))
    print('net.blobs: {0}'.format(net.blobs))
    print('net.params: {0}'.format(net.params))    

def get_predicted_output(deploy_prototxt_filename, caffemodel_filename, input, net = None):
    '''
    Get the predicted output, i.e. perform a forward pass
    '''
    if net is None:
        net = caffe.Net(deploy_prototxt_filename,caffemodel_filename, caffe.TEST)

    #input = np.array([[ 5.1,  3.5,  1.4,  0.2]])
    #input = np.random.random((1, 1, 1))
    #print(input)
    #print(input.shape)
    out = net.forward(data=input)
    #print('out: {0}'.format(out))
    return out[net.outputs[0]]


import google.protobuf 
def print_network(prototxt_filename, caffemodel_filename):
    '''
    Draw the ANN architecture
    '''
    _net = caffe.proto.caffe_pb2.NetParameter()
    f = open(prototxt_filename)
    google.protobuf.text_format.Merge(f.read(), _net)
    caffe.draw.draw_net_to_file(_net, prototxt_filename + '.png' )
    print('Draw ANN done!')


def print_network_weights(prototxt_filename, caffemodel_filename):
    '''
    For each ANN layer, print weight heatmap and weight histogram 
    '''
    net = caffe.Net(prototxt_filename,caffemodel_filename, caffe.TEST)
    for layer_name in net.params: 
        # weights heatmap 
        arr = net.params[layer_name][0].data
        plt.clf()
        fig = plt.figure(figsize=(10,10))
        ax = fig.add_subplot(111)
        cax = ax.matshow(arr, interpolation='none')
        fig.colorbar(cax, orientation="horizontal")
        plt.savefig('{0}_weights_{1}.png'.format(caffemodel_filename, layer_name), dpi=100, format='png', bbox_inches='tight') # use format='svg' or 'pdf' for vectorial pictures
        plt.close()

        # weights histogram  
        plt.clf()
        plt.hist(arr.tolist(), bins=20)
        plt.savefig('{0}_weights_hist_{1}.png'.format(caffemodel_filename, layer_name), dpi=100, format='png', bbox_inches='tight') # use format='svg' or 'pdf' for vectorial pictures
        plt.close()


def get_predicted_outputs(deploy_prototxt_filename, caffemodel_filename, inputs):
    '''
    Get several predicted outputs
    '''
    outputs = []
    net = caffe.Net(deploy_prototxt_filename,caffemodel_filename, caffe.TEST)
    for input in inputs:
        #print(input)
        outputs.append(copy.deepcopy(get_predicted_output(deploy_prototxt_filename, caffemodel_filename, input, net)))
    return outputs    


def get_accuracy(true_outputs, predicted_outputs):
    '''

    '''
    number_of_samples = true_outputs.shape[0]
    number_of_outputs = true_outputs.shape[1]
    threshold = 0.0 # 0 if SigmoidCrossEntropyLoss ; 0.5 if EuclideanLoss
    for output_number in range(number_of_outputs):
        predicted_output_binary = []
        for sample_number in range(number_of_samples):
            #print(predicted_outputs)
            #print(predicted_outputs[sample_number][output_number])            
            if predicted_outputs[sample_number][0][output_number] < threshold:
                predicted_output = 0
            else:
                predicted_output = 1
            predicted_output_binary.append(predicted_output)

        print('accuracy: {0}'.format(sklearn.metrics.accuracy_score(true_outputs[:, output_number], predicted_output_binary)))
        print(sklearn.metrics.confusion_matrix(true_outputs[:, output_number], predicted_output_binary))


def main():
    '''
    This is the main function
    '''

    # Set parameters
    solver_prototxt_filename = 'iris_solver.prototxt'
    train_test_prototxt_filename = 'iris_train_test.prototxt'
    deploy_prototxt_filename  = 'iris_deploy.prototxt'
    deploy_prototxt_filename  = 'iris_deploy.prototxt'
    deploy_prototxt_batch2_filename  = 'iris_deploy_batchsize2.prototxt'
    hdf5_train_data_filename = 'iris_train_data.hdf5' 
    hdf5_test_data_filename = 'iris_test_data.hdf5' 
    caffemodel_filename = 'iris__iter_5000.caffemodel' # generated by train()

    # Prepare data
    data = load_data()
    print(data)
    train_data = data
    test_data = data
    save_data_as_hdf5(hdf5_train_data_filename, data)
    save_data_as_hdf5(hdf5_test_data_filename, data)

    # Train network
    train(solver_prototxt_filename)

    # Print network
    print_network(deploy_prototxt_filename, caffemodel_filename)
    print_network(train_test_prototxt_filename, caffemodel_filename)
    print_network_weights(train_test_prototxt_filename, caffemodel_filename)

    # Compute performance metrics
    #inputs = input = np.array([[[[ 5.1,  3.5,  1.4,  0.2]]],[[[ 5.9,  3. ,  5.1,  1.8]]]])
    inputs = data['input']
    outputs = get_predicted_outputs(deploy_prototxt_filename, caffemodel_filename, inputs)
    get_accuracy(data['output'], outputs)


if __name__ == "__main__":
    main()

इसके लिए दो निम्नलिखित iris_train_test.prototxt और iris_deploy.prototxt का एक ही फ़ोल्डर में होना आवश्यक है।

iris_train_test.prototxt :

name: "IrisNet"
layer {
  name: "iris"
  type: "HDF5Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  hdf5_data_param {
    source: "iris_train_data.txt"
    batch_size: 1

  }
}

layer {
  name: "iris"
  type: "HDF5Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  hdf5_data_param {
    source: "iris_test_data.txt"
    batch_size: 1

  }
}




layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "data"
  top: "ip1"
  param {
    lr_mult: 1  # the learning rate multiplier for weights
  }
  param {
    lr_mult: 2  # the learning rate multiplier for biases
  }
  inner_product_param {
    num_output: 50
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "drop1"
  type: "Dropout"
  bottom: "ip1"
  top: "ip1"
  dropout_param {
    dropout_ratio: 0.5
  }
}


layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 50
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "drop2"
  type: "Dropout"
  bottom: "ip2"
  top: "ip2"
  dropout_param {
    dropout_ratio: 0.4
  }
}



layer {
  name: "ip3"
  type: "InnerProduct"
  bottom: "ip2"
  top: "ip3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "drop3"
  type: "Dropout"
  bottom: "ip3"
  top: "ip3"
  dropout_param {
    dropout_ratio: 0.3
  }
}

layer {
  name: "loss"
  type: "SigmoidCrossEntropyLoss" 
  # type: "EuclideanLoss" 
  # type: "HingeLoss"  
  bottom: "ip3"
  bottom: "label"
  top: "loss"
}

iris_deploy.prototxt :

name: "IrisNet"
input: "data"
input_dim: 1 # batch size
input_dim: 1
input_dim: 1
input_dim: 4


layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "data"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 50
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "ip1"
  top: "ip1"
}
layer {
  name: "drop1"
  type: "Dropout"
  bottom: "ip1"
  top: "ip1"
  dropout_param {
    dropout_ratio: 0.5
  }
}


layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 50
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "drop2"
  type: "Dropout"
  bottom: "ip2"
  top: "ip2"
  dropout_param {
    dropout_ratio: 0.4
  }
}


layer {
  name: "ip3"
  type: "InnerProduct"
  bottom: "ip2"
  top: "ip3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "drop3"
  type: "Dropout"
  bottom: "ip3"
  top: "ip3"
  dropout_param {
    dropout_ratio: 0.3
  }
}

iris_solver.prototxt :

# The train/test net protocol buffer definition
net: "iris_train_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
test_iter: 1
# Carry out testing every test_interval training iterations.
test_interval: 1000
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.0001
momentum: 0.001
weight_decay: 0.0005
# The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
# Display every 100 iterations
display: 1000
# The maximum number of iterations
max_iter: 5000
# snapshot intermediate results
snapshot: 5000
snapshot_prefix: "iris_"
# solver mode: CPU or GPU
solver_mode: CPU # GPU