未能在Python中将NumPy数组转换为张量(不支持对象类型浮点数)

Question

我试图用Keras构建一个MLP，然后出现一个错误。我没有神经网络的经验，所以这对我来说很困难。过了一段时间后，当我为NN运行代码时，它会说：

'Failed to convert a NumPy array to a Tensor (Unsupported object type float) 
 in Python'

我拥有的代码(包括数据集的预处理)如下：

import pandas as pd
from tensorflow.keras.utils import get_file

pd.set_option('display.max_columns', 6)
pd.set_option('display.max_rows', 5)



dfs = []
for i in range(1,5):
    path = './UNSW-NB15_{}.csv'# There are 4 input csv files
    dfs.append(pd.read_csv(path.format(i), header = None))
all_data = pd.concat(dfs).reset_index(drop=True)  # Concat all to a single df

# This csv file contains names of all the features
df_col = pd.read_csv('./NUSW-NB15_features.csv', encoding='ISO-8859-1')
# Making column names lower case, removing spaces
df_col['Name'] = df_col['Name'].apply(lambda x: x.strip().replace(' ', '').lower())
# Renaming our dataframe with proper column names
all_data.columns = df_col['Name']


# display 5 rows
pd.set_option('display.max_columns', 48)
pd.set_option('display.max_rows', 21)
all_data

all_data['attack_cat'] = all_data['attack_cat'].str.strip()
all_data['attack_cat'] = all_data['attack_cat'].replace(['Backdoors'], 'Backdoor')
all_data.groupby('attack_cat')['attack_cat'].count()
all_data["attack_cat"] = all_data["attack_cat"].fillna('Normal')
all_data.groupby('attack_cat')['attack_cat'].count()
all_data.drop(all_data[all_data['is_ftp_login'] >= 2.0].index, inplace = True)


all_data.drop(['srcip', 'sport', 'dstip', 'dsport'],axis=1, inplace=True)

df = pd.concat([all_data,pd.get_dummies(all_data['proto'],prefix='proto')],axis=1)
df.drop('proto',axis=1, inplace=True)

df_2 = pd.concat([df,pd.get_dummies(df['state'],prefix='state')],axis=1)
df_2.drop('state',axis=1, inplace=True)

df_encoded = pd.concat([df_2,pd.get_dummies(df_2['service'],prefix='service')],axis=1)
df_encoded.drop('service',axis=1, inplace=True)

df_encoded['ct_flw_http_mthd'] = df_encoded['ct_flw_http_mthd'].fillna(0)
df_encoded['is_ftp_login'] = df_encoded['is_ftp_login'].fillna(0)

df = pd.DataFrame(df_encoded)
temp_cols=df_encoded.columns.tolist()
index=df.columns.get_loc("attack_cat")
new_cols=temp_cols[0:index] + temp_cols[index+1:] + temp_cols[index:index+1]
df=df_encoded[new_cols]

df_encoded = df.drop('label', axis=1)


x_columns = df_encoded.columns.drop('attack_cat')
x = df_encoded[x_columns].values
dummies = pd.get_dummies(df['attack_cat'])
products = dummies.columns
y = dummies.values

import numpy as np
import tensorflow.keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping
from sklearn import metrics

x_train, x_test, y_train, y_test = train_test_split(    
    x, y, test_size=0.25, random_state=42)

model = Sequential()
model.add(Dense(10, input_dim= x.shape[1], activation= 'relu'))
model.add(Dense(9, activation= 'relu'))
model.add(Dense(9,activation= 'relu'))
model.add(Dense(y_train.shape[1],activation= 'softmax', kernel_initializer='normal'))
model.compile(loss= 'categorical_crossentropy', optimizer= 'adam', metrics= ['accuracy'])
monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, 
                        verbose=1, mode='auto', restore_best_weights=True)

model.fit(x_train,y_train,validation_data=(x_test,y_test),
          callbacks=[monitor],verbose=2, epochs=1000)


pred = model.predict(x_test)
pred = np.argmax(pred,axis=1) 
y_compare = np.argmax(y_test,axis=1) 
score = metrics.accuracy_score(y_compare, pred)
print("Accuracy score: {}".format(score))

我使用的数据集是UNSW 15 (2+百万输入)

该错误出现在执行最后一个代码块后(从导入numpy开始)

谢谢你能给我解决这个问题的任何建议。

穆罕默德提供的更新后出现的错误如下：

ValueError                                Traceback (most recent call last)
Input In [13], in <cell line: 22>()
     18 model.compile(loss= 'categorical_crossentropy', optimizer= 'adam', metrics= ['accuracy'])
     19 monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5, 
     20                         verbose=1, mode='auto', restore_best_weights=True)
---> 22 model.fit(tf.cast(x_train,dtype=tf.float32),y_train,validation_data=(x_test,y_test),
     23           callbacks=[monitor],verbose=2, epochs=1000)
     26 pred = model.predict(x_test)
     27 pred = np.argmax(pred,axis=1) 

File ~\miniconda3\envs\pruebas\lib\site-packages\tensorflow\python\util\dispatch.py:206, in add_dispatch_support.<locals>.wrapper(*args, **kwargs)
    204 """Call target, and fall back on dispatchers if there is a TypeError."""
    205 try:
--> 206   return target(*args, **kwargs)
    207 except (TypeError, ValueError):
    208   # Note: convert_to_eager_tensor currently raises a ValueError, not a
    209   # TypeError, when given unexpected types.  So we need to catch both.
    210   result = dispatch(wrapper, args, kwargs)

File ~\miniconda3\envs\pruebas\lib\site-packages\tensorflow\python\ops\math_ops.py:988, in cast(x, dtype, name)
    982   x = ops.IndexedSlices(values_cast, x.indices, x.dense_shape)
    983 else:
    984   # TODO(josh11b): If x is not already a Tensor, we could return
    985   # ops.convert_to_tensor(x, dtype=dtype, ...)  here, but that
    986   # allows some conversions that cast() can't do, e.g. casting numbers to
    987   # strings.
--> 988   x = ops.convert_to_tensor(x, name="x")
    989   if x.dtype.base_dtype != base_type:
    990     x = gen_math_ops.cast(x, base_type, name=name)

File ~\miniconda3\envs\pruebas\lib\site-packages\tensorflow\python\profiler\trace.py:163, in trace_wrapper.<locals>.inner_wrapper.<locals>.wrapped(*args, **kwargs)
    161   with Trace(trace_name, **trace_kwargs):
    162     return func(*args, **kwargs)
--> 163 return func(*args, **kwargs)

File ~\miniconda3\envs\pruebas\lib\site-packages\tensorflow\python\framework\ops.py:1566, in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
   1561       raise TypeError("convert_to_tensor did not convert to "
   1562                       "the preferred dtype: %s vs %s " %
   1563                       (ret.dtype.base_dtype, preferred_dtype.base_dtype))
   1565 if ret is None:
-> 1566   ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
   1568 if ret is NotImplemented:
   1569   continue

File ~\miniconda3\envs\pruebas\lib\site-packages\tensorflow\python\framework\tensor_conversion_registry.py:52, in _default_conversion_function(***failed resolving arguments***)
     50 def _default_conversion_function(value, dtype, name, as_ref):
     51   del as_ref  # Unused.
---> 52   return constant_op.constant(value, dtype, name=name)

File ~\miniconda3\envs\pruebas\lib\site-packages\tensorflow\python\framework\constant_op.py:271, in constant(value, dtype, shape, name)
    174 @tf_export("constant", v1=[])
    175 def constant(value, dtype=None, shape=None, name="Const"):
    176   """Creates a constant tensor from a tensor-like object.
    177 
    178   Note: All eager `tf.Tensor` values are immutable (in contrast to
   (...)
    269     ValueError: if called on a symbolic tensor.
    270   """
--> 271   return _constant_impl(value, dtype, shape, name, verify_shape=False,
    272                         allow_broadcast=True)

File ~\miniconda3\envs\pruebas\lib\site-packages\tensorflow\python\framework\constant_op.py:283, in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
    281     with trace.Trace("tf.constant"):
    282       return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
--> 283   return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
    285 g = ops.get_default_graph()
    286 tensor_value = attr_value_pb2.AttrValue()

File ~\miniconda3\envs\pruebas\lib\site-packages\tensorflow\python\framework\constant_op.py:308, in _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
    306 def _constant_eager_impl(ctx, value, dtype, shape, verify_shape):
    307   """Creates a constant on the current device."""
--> 308   t = convert_to_eager_tensor(value, ctx, dtype)
    309   if shape is None:
    310     return t

File ~\miniconda3\envs\pruebas\lib\site-packages\tensorflow\python\framework\constant_op.py:106, in convert_to_eager_tensor(value, ctx, dtype)
    104     dtype = dtypes.as_dtype(dtype).as_datatype_enum
    105 ctx.ensure_initialized()
--> 106 return ops.EagerTensor(value, ctx.device_name, dtype)

ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float).

列类型：

df_encoded.info(verbose=True)


<class 'pandas.core.frame.DataFrame'>
Int64Index: 2539861 entries, 0 to 2540046
Data columns (total 205 columns):
 #    Column             Dtype  
---   ------             -----  
 0    dur                float64
 1    sbytes             int64  
 2    dbytes             int64  
 3    sttl               int64  
 4    dttl               int64  
 5    sloss              int64  
 6    dloss              int64  
 7    sload              float64
 8    dload              float64
 9    spkts              int64  
 10   dpkts              int64  
 11   swin               int64  
 12   dwin               int64  
 13   stcpb              int64  
 14   dtcpb              int64  
 15   smeansz            int64  
 16   dmeansz            int64  
 17   trans_depth        int64  
 18   res_bdy_len        int64  
 19   sjit               float64
 20   djit               float64
 21   stime              int64  
 22   ltime              int64  
 23   sintpkt            float64
 24   dintpkt            float64
 25   tcprtt             float64
 26   synack             float64
 27   ackdat             float64
 28   is_sm_ips_ports    int64  
 29   ct_state_ttl       int64  
 30   ct_flw_http_mthd   float64
 31   is_ftp_login       float64
 32   ct_ftp_cmd         object 
 33   ct_srv_src         int64  
 34   ct_srv_dst         int64  
 35   ct_dst_ltm         int64  
 36   ct_src_ltm         int64  
 37   ct_src_dport_ltm   int64  
 38   ct_dst_sport_ltm   int64  
 39   ct_dst_src_ltm     int64  
 40   proto_3pc          uint8  
 41   proto_a/n          uint8  
 42   proto_aes-sp3-d    uint8  
 43   proto_any          uint8  
 44   proto_argus        uint8  
 45   proto_aris         uint8  
 46   proto_arp          uint8  
 47   proto_ax.25        uint8  
 48   proto_bbn-rcc      uint8  
 49   proto_bna          uint8  
 50   proto_br-sat-mon   uint8  
 51   proto_cbt          uint8  
 52   proto_cftp         uint8  
 53   proto_chaos        uint8  
 54   proto_compaq-peer  uint8  
 55   proto_cphb         uint8  
 56   proto_cpnx         uint8  
 57   proto_crtp         uint8  
 58   proto_crudp        uint8  
 59   proto_dcn          uint8  
 60   proto_ddp          uint8  
 61   proto_ddx          uint8  
 62   proto_dgp          uint8  
 63   proto_egp          uint8  
 64   proto_eigrp        uint8  
 65   proto_emcon        uint8  
 66   proto_encap        uint8  
 67   proto_esp          uint8  
 68   proto_etherip      uint8  
 69   proto_fc           uint8  
 70   proto_fire         uint8  
 71   proto_ggp          uint8  
 72   proto_gmtp         uint8  
 73   proto_gre          uint8  
 74   proto_hmp          uint8  
 75   proto_i-nlsp       uint8  
 76   proto_iatp         uint8  
 77   proto_ib           uint8  
 78   proto_icmp         uint8  
 79   proto_idpr         uint8  
 80   proto_idpr-cmtp    uint8  
 81   proto_idrp         uint8  
 82   proto_ifmp         uint8  
 83   proto_igmp         uint8  
 84   proto_igp          uint8  
 85   proto_il           uint8  
 86   proto_ip           uint8  
 87   proto_ipcomp       uint8  
 88   proto_ipcv         uint8  
 89   proto_ipip         uint8  
 90   proto_iplt         uint8  
 91   proto_ipnip        uint8  
 92   proto_ippc         uint8  
 93   proto_ipv6         uint8  
 94   proto_ipv6-frag    uint8  
 95   proto_ipv6-no      uint8  
 96   proto_ipv6-opts    uint8  
 97   proto_ipv6-route   uint8  
 98   proto_ipx-n-ip     uint8  
 99   proto_irtp         uint8  
 100  proto_isis         uint8  
 101  proto_iso-ip       uint8  
 102  proto_iso-tp4      uint8  
 103  proto_kryptolan    uint8  
 104  proto_l2tp         uint8  
 105  proto_larp         uint8  
 106  proto_leaf-1       uint8  
 107  proto_leaf-2       uint8  
 108  proto_merit-inp    uint8  
 109  proto_mfe-nsp      uint8  
 110  proto_mhrp         uint8  
 111  proto_micp         uint8  
 112  proto_mobile       uint8  
 113  proto_mtp          uint8  
 114  proto_mux          uint8  
 115  proto_narp         uint8  
 116  proto_netblt       uint8  
 117  proto_nsfnet-igp   uint8  
 118  proto_nvp          uint8  
 119  proto_ospf         uint8  
 120  proto_pgm          uint8  
 121  proto_pim          uint8  
 122  proto_pipe         uint8  
 123  proto_pnni         uint8  
 124  proto_pri-enc      uint8  
 125  proto_prm          uint8  
 126  proto_ptp          uint8  
 127  proto_pup          uint8  
 128  proto_pvp          uint8  
 129  proto_qnx          uint8  
 130  proto_rdp          uint8  
 131  proto_rsvp         uint8  
 132  proto_rtp          uint8  
 133  proto_rvd          uint8  
 134  proto_sat-expak    uint8  
 135  proto_sat-mon      uint8  
 136  proto_sccopmce     uint8  
 137  proto_scps         uint8  
 138  proto_sctp         uint8  
 139  proto_sdrp         uint8  
 140  proto_secure-vmtp  uint8  
 141  proto_sep          uint8  
 142  proto_skip         uint8  
 143  proto_sm           uint8  
 144  proto_smp          uint8  
 145  proto_snp          uint8  
 146  proto_sprite-rpc   uint8  
 147  proto_sps          uint8  
 148  proto_srp          uint8  
 149  proto_st2          uint8  
 150  proto_stp          uint8  
 151  proto_sun-nd       uint8  
 152  proto_swipe        uint8  
 153  proto_tcf          uint8  
 154  proto_tcp          uint8  
 155  proto_tlsp         uint8  
 156  proto_tp++         uint8  
 157  proto_trunk-1      uint8  
 158  proto_trunk-2      uint8  
 159  proto_ttp          uint8  
 160  proto_udp          uint8  
 161  proto_udt          uint8  
 162  proto_unas         uint8  
 163  proto_uti          uint8  
 164  proto_vines        uint8  
 165  proto_visa         uint8  
 166  proto_vmtp         uint8  
 167  proto_vrrp         uint8  
 168  proto_wb-expak     uint8  
 169  proto_wb-mon       uint8  
 170  proto_wsn          uint8  
 171  proto_xnet         uint8  
 172  proto_xns-idp      uint8  
 173  proto_xtp          uint8  
 174  proto_zero         uint8  
 175  state_ACC          uint8  
 176  state_CLO          uint8  
 177  state_CON          uint8  
 178  state_ECO          uint8  
 179  state_ECR          uint8  
 180  state_FIN          uint8  
 181  state_INT          uint8  
 182  state_MAS          uint8  
 183  state_PAR          uint8  
 184  state_REQ          uint8  
 185  state_RST          uint8  
 186  state_TST          uint8  
 187  state_TXD          uint8  
 188  state_URH          uint8  
 189  state_URN          uint8  
 190  state_no           uint8  
 191  service_-          uint8  
 192  service_dhcp       uint8  
 193  service_dns        uint8  
 194  service_ftp        uint8  
 195  service_ftp-data   uint8  
 196  service_http       uint8  
 197  service_irc        uint8  
 198  service_pop3       uint8  
 199  service_radius     uint8  
 200  service_smtp       uint8  
 201  service_snmp       uint8  
 202  service_ssh        uint8  
 203  service_ssl        uint8  
 204  attack_cat         object 
dtypes: float64(12), int64(27), object(2), uint8(164)
memory usage: 1.2+ GB

删除ct_ftp_cmd后出现的新错误：

TypeError                                 Traceback (most recent call last)
Input In [17], in <cell line: 12>()
      8 from sklearn import metrics
     10 x_cast = tf.cast(x,dtype=tf.float32)
---> 12 x_train, x_test, y_train, y_test = train_test_split(    
     13     x_cast, y, test_size=0.25, random_state=42)
     16 model = Sequential()
     17 model.add(Dense(10, input_dim= x.shape[1], activation= 'relu'))

File ~\miniconda3\envs\pruebas\lib\site-packages\sklearn\model_selection\_split.py:2443, in train_test_split(test_size, train_size, random_state, shuffle, stratify, *arrays)
   2439     cv = CVClass(test_size=n_test, train_size=n_train, random_state=random_state)
   2441     train, test = next(cv.split(X=arrays[0], y=stratify))
-> 2443 return list(
   2444     chain.from_iterable(
   2445         (_safe_indexing(a, train), _safe_indexing(a, test)) for a in arrays
   2446     )
   2447 )

File ~\miniconda3\envs\pruebas\lib\site-packages\sklearn\model_selection\_split.py:2445, in <genexpr>(.0)
   2439     cv = CVClass(test_size=n_test, train_size=n_train, random_state=random_state)
   2441     train, test = next(cv.split(X=arrays[0], y=stratify))
   2443 return list(
   2444     chain.from_iterable(
-> 2445         (_safe_indexing(a, train), _safe_indexing(a, test)) for a in arrays
   2446     )
   2447 )

File ~\miniconda3\envs\pruebas\lib\site-packages\sklearn\utils\__init__.py:378, in _safe_indexing(X, indices, axis)
    376     return _pandas_indexing(X, indices, indices_dtype, axis=axis)
    377 elif hasattr(X, "shape"):
--> 378     return _array_indexing(X, indices, indices_dtype, axis=axis)
    379 else:
    380     return _list_indexing(X, indices, indices_dtype)

File ~\miniconda3\envs\pruebas\lib\site-packages\sklearn\utils\__init__.py:202, in _array_indexing(array, key, key_dtype, axis)
    200 if isinstance(key, tuple):
    201     key = list(key)
--> 202 return array[key] if axis == 0 else array[:, key]

File ~\miniconda3\envs\pruebas\lib\site-packages\tensorflow\python\util\dispatch.py:206, in add_dispatch_support.<locals>.wrapper(*args, **kwargs)
    204 """Call target, and fall back on dispatchers if there is a TypeError."""
    205 try:
--> 206   return target(*args, **kwargs)
    207 except (TypeError, ValueError):
    208   # Note: convert_to_eager_tensor currently raises a ValueError, not a
    209   # TypeError, when given unexpected types.  So we need to catch both.
    210   result = dispatch(wrapper, args, kwargs)

File ~\miniconda3\envs\pruebas\lib\site-packages\tensorflow\python\ops\array_ops.py:1014, in _slice_helper(tensor, slice_spec, var)
   1012   new_axis_mask |= (1 << index)
   1013 else:
-> 1014   _check_index(s)
   1015   begin.append(s)
   1016   end.append(s + 1)

File ~\miniconda3\envs\pruebas\lib\site-packages\tensorflow\python\ops\array_ops.py:888, in _check_index(idx)
    883 dtype = getattr(idx, "dtype", None)
    884 if (dtype is None or dtypes.as_dtype(dtype) not in _SUPPORTED_SLICE_DTYPES or
    885     idx.shape and len(idx.shape) == 1):
    886   # TODO(slebedev): IndexError seems more appropriate here, but it
    887   # will break `_slice_helper` contract.
--> 888   raise TypeError(_SLICE_TYPE_ERROR + ", got {!r}".format(idx))

TypeError: Only integers, slices (`:`), ellipsis (`...`), tf.newaxis (`None`) and scalar tf.int32/tf.int64 tensors are valid indices, got array([ 214948, 2349007,  452929, ..., 2356330, 2229084, 2219110])
```

Answer 1

将tf.keras.regularizer.l1(0.1)添加到密集的层中。可能这会增加您的时代数，并尝试在GPU下的COLAB设置中运行它。